Package 'switchSelection' reference manual

Title:	Endogenous Switching and Sample Selection Regression Models
Description:	Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) <doi:10.1111/j.1368-423X.2008.00263.x>, E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>, E. Kossova, L. Kupriianova, B. Potanin (2020) <https://ideas.repec.org/a/ris/apltrx/0391.html> and E. Kossova, B. Potanin (2022) <https://ideas.repec.org/a/ris/apltrx/0455.html>.
Authors:	Bogdan Potanin [aut, cre, ctb], Sofiia Dolgikh [ctb]
Maintainer:	Bogdan Potanin <[email protected]>
License:	GPL (>= 2)
Version:	2.0.0
Built:	2024-10-27 03:48:34 UTC
Source:	https://github.com/cran/switchSelection

Bootstrap covariance matrix for least squares estimates of linear regression

Description

This function calculates bootstrapped covariance matrix for least squares estimates of linear regression. The estimates should be obtained via lm function.

Usage

boot(model, iter = 100)
boot(model, iter = 100)

Arguments

`model`	object of class `lm`.
`iter`	positive integer representing the number of bootstrap iterations.

Details

Calculations may take long time for high iter value.

Value

This function returns a bootstrapped covariance matrix of the least squares estimator.

Examples

set.seed(123)
# Generate data according to linear regression
n <- 20
eps <- rnorm(n)
x <- runif(n)
y <- x + eps
# Estimate the model
model <- lm(y ~ x)
# Calculate bootstrap covariance matrix
boot(model, iter = 50)
set.seed(123)
# Generate data according to linear regression
n <- 20
eps <- rnorm(n)
x <- runif(n)
y <- x + eps
# Estimate the model
model <- lm(y ~ x)
# Calculate bootstrap covariance matrix
boot(model, iter = 50)

Bootstrap for msel function

Description

Function bootstrap_msel provides bootstrap estimates of the parameters of the model estimated via the msel function. Function bootstrap_combine_msel allows to combine several objects of class 'bootstrap_msel'.

Usage

bootstrap_msel(
  object,
  iter = 100,
  opt_type = "optim",
  opt_args = NULL,
  is_ind = FALSE,
  n_sim = 1000,
  n_cores = 1
)

bootstrap_combine_msel(...)
bootstrap_msel(
  object,
  iter = 100,
  opt_type = "optim",
  opt_args = NULL,
  is_ind = FALSE,
  n_sim = 1000,
  n_cores = 1
)

bootstrap_combine_msel(...)

Arguments

`object`	an object of class 'msel'.
`iter`	the number of bootstrap iterations.
`opt_type`	the same as `opt_type` argument of the `msel` function.
`opt_args`	the same as `opt_args` argument of the `msel` functions.
`is_ind`	logical; if `TRUE` then the function also returns a numeric matrix of indexes of observations used in the bootstrap samples.
`n_sim`	the same as `n_sim` argument of the `msel` function.
`n_cores`	the same as `n_cores` argument of the `msel` function.
`...`	objects returned by function `bootstrap_msel` to be combined into a single object.

Details

The function generates iter bootstrap samples and estimates the parameters $\theta$ of the model by using each of these samples. Estimate $\hat{\theta}^{(b)}$ from the $b$ -th of these samples is stored as the b-th row of the numeric matrix par which is an element of the returned object.

Use update_msel function to transfer the bootstrap estimates to the object of class 'msel'.

Value

Function bootstrap_msel returns an object of class "bootstrap_msel". This object is a list which may contain the following elements:

par - a numeric matrix such that par[b, ] is a vector of the estimates of the parameters of the model estimated via msel function on the b-th bootstrap sample.
iter - the number of the bootstrap iterations.
cov - bootstrap estimate of the covariance matrix which equals to cov(par).
ind - a numeric matrix such that ind[, b] stores the indexes of the observations from object$data included into the b-th bootstrap sample.

Function bootstrap_combine_msel returns the object which combines several objects returned by the bootstrap_msel function into a single object.

Examples

# -------------------------------
# Bootstrap for the probit model
# -------------------------------

# ---
# Step 1
# Simulation of data
# ---

# Load required package
library("mnorm")

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 100

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)

# Random errors
u <- rnorm(n = n, mean = 0, sd = 1)

# Coefficients
gamma <- c(-1, 2)

# Linear index
li <- gamma[1] * w1 + gamma[2] * w2

# Latent variable
z_star <- li + u

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordered outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Data
data <- data.frame(w1 = w1, w2 = w2, z = z)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(formula = z ~ w1 + w2, data = data)
summary(model)
 
# ---
# Step 3
# Bootstrap
# ---

# Perform bootstrap
bootstrap <- bootstrap_msel(model)

# Test the hypothesis that H0: gamma[2] = -2gamma[1]
# by using the t-test and with bootstrap p-values
fn_test <- function(object)
{
  gamma <- coef(object, eq = 1)
  return(gamma[2] + 2 * gamma[1])
}
b <- test_msel(object    = model, 
               fn        = fn_test,
               test      = "t", 
               method    = "bootstrap",
               ci        = "percentile",
               se_type   = "bootstrap",
               bootstrap = bootstrap)
summary(b)

# Replicate the analysis with the additional 20 bootstrap iterations
bootstrap2    <- bootstrap_msel(model, iter = 20)
bootstrap_new <- bootstrap_combine_msel(bootstrap, bootstrap2)
b2 <- test_msel(object    = model, 
                fn        = fn_test,
                test      = "t", 
                method    = "bootstrap",
                ci        = "percentile",
                se_type   = "bootstrap",
                bootstrap = bootstrap)
summary(b2)

 
# -------------------------------
# Bootstrap for the probit model
# -------------------------------

# ---
# Step 1
# Simulation of data
# ---

# Load required package
library("mnorm")

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 100

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)

# Random errors
u <- rnorm(n = n, mean = 0, sd = 1)

# Coefficients
gamma <- c(-1, 2)

# Linear index
li <- gamma[1] * w1 + gamma[2] * w2

# Latent variable
z_star <- li + u

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordered outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Data
data <- data.frame(w1 = w1, w2 = w2, z = z)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(formula = z ~ w1 + w2, data = data)
summary(model)
 
# ---
# Step 3
# Bootstrap
# ---

# Perform bootstrap
bootstrap <- bootstrap_msel(model)

# Test the hypothesis that H0: gamma[2] = -2gamma[1]
# by using the t-test and with bootstrap p-values
fn_test <- function(object)
{
  gamma <- coef(object, eq = 1)
  return(gamma[2] + 2 * gamma[1])
}
b <- test_msel(object    = model, 
               fn        = fn_test,
               test      = "t", 
               method    = "bootstrap",
               ci        = "percentile",
               se_type   = "bootstrap",
               bootstrap = bootstrap)
summary(b)

# Replicate the analysis with the additional 20 bootstrap iterations
bootstrap2    <- bootstrap_msel(model, iter = 20)
bootstrap_new <- bootstrap_combine_msel(bootstrap, bootstrap2)
b2 <- test_msel(object    = model, 
                fn        = fn_test,
                test      = "t", 
                method    = "bootstrap",
                ci        = "percentile",
                se_type   = "bootstrap",
                bootstrap = bootstrap)
summary(b2)

Coefficients extraction method for msel.

Description

Extract coefficients and other estimates from msel object.

Usage

## S3 method for class 'msel'
coef(
  object,
  ...,
  eq = NULL,
  eq2 = NULL,
  eq3 = NULL,
  regime = NULL,
  type = "coef"
)
## S3 method for class 'msel'
coef(
  object,
  ...,
  eq = NULL,
  eq2 = NULL,
  eq3 = NULL,
  regime = NULL,
  type = "coef"
)

Arguments

`object`	an object of class "msel".
`...`	further arguments (currently ignored).
`eq`	an integer representing the index of the ordered equation.
`eq2`	an integer representing the index of the continuous equation.
`eq3`	an integer representing the index of the alternative of the multinomial equation.
`regime`	an integer representing a regime of the continuous equation.
`type`	a character representing a type of the output. Possible options are `"coef"`, `"coef2"`, `coef_lambda`, `"coef_var"`, `"coef3"`, `"cuts"`, `"cov"`, `"cov1"`, `"var"`, `"cov2"`, `"cov3"`, and `marginal`. See 'Details' for additional information.

Details

Consider the notations from the 'Details' section of msel.

Mean coefficients of the ordinal equations

Suppose that type = "coef". Then estimates of the $\gamma_{j}$ coefficients are returned for each $j\in\{1,...,J\}$ . If eq = j then only estimates of the $\gamma_{j}$ coefficients are returned.

Variance coefficients of the ordinal equations

Suppose that type = "coef_var". Then estimates of the $\gamma_{j}^{*}$ coefficients are returned for each $j\in\{1,...,J\}$ . If eq = j then only estimates of $\gamma_{j}^{*}$ coefficients are returned.

Coefficients of the continuous equations

Suppose that type = "coef2". Then estimates of the $\beta_{r}$ coefficients are returned for each $r\in\{0,...,R - 1\}$ . If eq2 = k then only estimates for the $k$ -th continuous equation are returned. If regime = r then estimates of the $\beta_{r}$ coefficients are returned for the eq2-th continuous equation. Herewith if regime is not NULL and eq2 is NULL it is assumed that eq2 = 1.

Selectivity terms

Suppose that type = "coef_lambda". Then estimates of the coefficients associated with the selectivity terms are returned for each $r\in\{0,...,R - 1\}$ . If eq2 = k then only estimates for the $k$ -th continuous equation are returned. If regime = r then estimates of the coefficients of the selectivity terms are returned for the eq2-th continuous equation.

Thresholds of the ordinal equations

Suppose that type = "cuts" or type = "thresholds". Then estimates of the $c_{j}$ cuts (thresholds) are returned for each $j\in\{1,...,J\}$ . If eq = j then only estimates of the $c_{j}$ cuts are returned.

Covariances between the random errors of the ordinal equations

Suppose that type = "cov1". Then estimate of the covariance matrix of $u_{i}$ is returned. If eq = c(a, b) then the function returns $(a, b)$ -th element of this matrix i.e. an element from the a-th row and the b-th column which represents an estimate of $Cov(u_{ai}, u_{bi})$ .

Covariances between the random errors of the ordinal and continuous equations

Suppose that type = "cov12". Then estimates of the covariances between random errors of the ordinal $u_{i}$ and cotninuous $\varepsilon_{i}$ equations are returned. If eq2 = k then covariances with random errors of the k-th continuous equation are returned. If in addition eq = j and regime = r then the function returns an estimate of $Cov(u_{ji}, \varepsilon_{ri})$ for the k-th continuous equation. If eq2 = NULL it is assumed that eq2 = 1.

Variances of the random errors of the continuous equations

Suppose that type = "var". Then estimates of the variances of $\varepsilon_{i}$ are returned. If eq2 = k then estimates only for the $k$ -th continuous equation are returned. If in addition regime = r then estimate of the $Var(\varepsilon_{ri})$ is returned. Herewith if regime is not NULL and eq2 is NULL it is assumed that eq2 = 1.

Covariances between the random errors of the continuous equations

Suppose that type = "cov2". Then estimates of the covariances between random errors of different continuous equations in different regimes are returned. If eq2 = c(a, b) and regime = c(c, d) then function returns an estimate of the covariance of random errors of the a-th and b-th continuous equations in the regimes c and d correspondingly. If this covariance is not identifiable then NA value is returned.

Coefficients of the multinomial equation

Suppose that type = "coef3". Then estimates of the $\tilde{\gamma}_{j}$ coefficients are returned for each $j\in\{0,...,\tilde{J} - 1\}$ . If eq3 = j then only estimates of the $\tilde{\gamma}_{j}$ coefficients are returned.

Covariances between the random errors of the multinomial equations

Suppose that type = "cov3". Then estimate of the covariance matrix of $\tilde{u}_{i}$ is returned. If eq3 = c(a, b) then the function returns $(a, b)$ -th element of this matrix i.e. an element from the a-th row and the b-th column which represents an estimate of $Cov(\tilde{u}_{(a+1)i}, \tilde{u}_{(b+1)i})$ .

Parameters of the marginal distributions

Suppose that type = "marginal". Then a list is returned which j-th element is a numeric vector of estimates of the parameters of the marginal distribution of $u_{ji}$ .

Asymptotic covariance matrix

Suppose that type = "cov". Then estimate of the asymptotic covariance matrix of the estimator is returned. Note that this estimate depends on the cov_type argument of msel.

Value

See 'Details' section.

A subset of data from Current Population Survey (CPS).

Description

Labor market data on 18,253 middle age (25-54 years) married women in the year 2022.

Usage

data(cps)
data(cps)

Format

A data frame with 18,253 rows and 23 columns. It contains information on wages and some socio-demographic characteristics of the middle age (25-54 years) married women:

age: the age measured in years.
sage: the same as age but for a spouse.
work: a binary variable for the employment status (0 - unemployed, 1 - employed).
swork: the same as work but for a spouse.
nchild: the number of children under age 5.
snchild: the same as nchild but for a spouse.
health: subjective health status (1 - poor, 2 - fair, 3 - good, 4 - very good, 5 - excellent).
shealth: the same as health but for a spouse.
basic: a binary variable which equals 1 for those who have graduated from high school or has at least some college or has associated degree and does not have any higher level of education, 0 - otherwise.
bachelor: a binary variable which equals 1 for those whose highest education level is a bachelor degree.
master: a binary variable which equals 1 for those whose highest education level is a master degree.
sbasic: the same as basic but for a spouse.
sbachelor: the same as bachelor but for a spouse.
smaster: the same as master but for a spouse.
educ: a categorical variable for the level of education such that educ = 0 if basic = 1, educ = 1 if bachelor = 1 and educ = 2 if master = 1.
seduc: the same as educ but for a spouse.
weeks: a total number of weeks worked durning the year.
sweeks: the same as weeks but for a spouse.
hours: a usual number of working hours per week.
shours: the same as hours but for a spouse.
wage: the wage of the individual.
swage: the same as wage but for a spouse.
lwage: an inverse hyperbolic sine transformation of the hourly wage.
slwage: the same as lwage but for a spouse.
state: a state of residence.

...

Source

<https://www.census.gov/programs-surveys/cps.html>

References

Flood S, King M, Rodgers R, Ruggles S, Warren R, Westberry M (2022). Integrated Public Use Microdata Series, Current Population Survey: Version 10.0 [dataset]. doi: 10.18128/D030.V10.0.

Examples


data(cps)
model <- msel(work ~ age + bachelor + master, data = cps)
summary(model)
 
data(cps)
model <- msel(work ~ age + bachelor + master, data = cps)
summary(model)

Modify exogenous variables in data frame

Description

Change some values of the exogenous variables in a data frame.

Usage

exogenous_fn(exogenous, newdata)
exogenous_fn(exogenous, newdata)

Arguments

exogenous

list such that exogenous[[i]] represents the value (or a vector of values of the same size as nrow(newdata)) which will be exogenously assigned to the variable names(exogenous)[[i]] in newdata i.e., newdata[, names(exogenous)[i]] <- exogenous[[i]]. If newdata is NULL and exogenous is not NULL then newdata is set to object$data. This argument is especially useful for causal inference when some endogenous (dependent) variables should be exogenously assigned with some values i.e., in the right hand side of the formula and formula2. The purpose of the exogeneous argument is just a convenience so equivalently it is possible to exogenously provide the values to variables via newdata argument.

newdata

data frame.

Details

This function changes exogenous variables in newdata.

Value

The function returns data frame which is similar to newdata but some values of this data frame are set according to exogenous.

Extract Model Fitted Values

Description

Extracts fitted values from 'msel' object

Usage

## S3 method for class 'msel'
fitted(object, ..., newdata = NULL)
## S3 method for class 'msel'
fitted(object, ..., newdata = NULL)

Arguments

`object`	object of class 'msel'.
`...`	further arguments (currently ignored).
`newdata`	an optional data frame in which to look for variables with which to predict. If omitted, the original data frame used. This data frame should contain values of dependent variables even if they are not actually needed for prediction (simply assign them with 0 values).

Value

Returns a numeric matrix. Its columns which names coincide with the names of the ordinal and multinomial equations provide the index of the most probable category for each observation. Columns which names coincide with the names of the continuous equations provide conditional expectations of the dependent variables in observable regimes for each observation.

Merge formulas

Description

This function merges all variables of several formulas into a single formula.

Usage

formula_merge(..., type = "all")
formula_merge(..., type = "all")

Arguments

...

formulas to be merged such that there is a single element on the left hand side and various elements on the right hand side.

type

string representing the type of merge to be used. If type = "all" then both right hand side and left hand side elements of the formulas will be merged on the right hand side. If type = "terms" then only right hand side elements of the formulas will be merged on the right hand side. If type = "var-terms" then the result is the same as in case when type = "terms" but there will be left hand side element of the first formula on the left hand side of the merged formula.

Details

Merged formulas should have a single element on the left hand side and voluntary number of elements on the right hand side.

Value

This function returns a formula which form depends on type input argument value. See 'Details' for additional information.

Examples

# Consider three formulas
f1 <- as.formula("y1 ~ x1 + x2")
f2 <- as.formula("y2 ~ x2 + x3")
f3 <- as.formula("y3 ~ y2 + x6")
# Merge these formulas in a various ways
formula_merge(f1, f2, f3, type = "all")
formula_merge(f1, f2, f3, type = "terms")
formula_merge(f1, f2, f3, type = "var-terms")

# Consider three formulas
f1 <- as.formula("y1 ~ x1 + x2")
f2 <- as.formula("y2 ~ x2 + x3")
f3 <- as.formula("y3 ~ y2 + x6")
# Merge these formulas in a various ways
formula_merge(f1, f2, f3, type = "all")
formula_merge(f1, f2, f3, type = "terms")
formula_merge(f1, f2, f3, type = "var-terms")

Split formula by symbol

Description

This function splits one formula into two formulas by symbol.

Usage

formula_split(formula, symbol = "|")
formula_split(formula, symbol = "|")

Arguments

`formula`	an object of class `formula`.
`symbol`	a string that is used to split `formula` into two formulas.

Details

The symbol should be on the right hand side of the formula.

Value

This function returns a list of two formulas.

Examples

formula_split("y ~ x1 + x2 | x2 + x3")
formula_split("y ~ x1 + x2 : x2 + x3", symbol = ":")

formula_split("y ~ x1 + x2 | x2 + x3")
formula_split("y ~ x1 + x2 : x2 + x3", symbol = ":")

Formulas of msel model.

Description

Provides formulas associated with the object of class 'msel'.

Usage

## S3 method for class 'msel'
formula(x, ..., type = "formula", eq = NULL)
## S3 method for class 'msel'
formula(x, ..., type = "formula", eq = NULL)

Arguments

`x`	object of class 'msel'.
`...`	further arguments (currently ignored).
`type`	character; if `type = "formula"` or `type = 1` then function returns formulas of the ordinal equations. If `type = "formula2"` or `type = 2` then function returns formulas of the continuous equations. If `type = "formula3"` or `type = 3` then function returns formula of the multinomial equation.
`eq`	positive integer representing the index of the equation which formula should be returned. If `NULL` (default) then formulas for each equation will be returned as a list which `i`-th element associated with `i`-th equation.

Value

Returns a formula or a list of formulas depending on eq value.

Gradient of the Log-likelihood Function of Multivariate Ordered Probit Model

Description

Calculates gradient of the log-likelihood function of multivariate ordered probit model.

Usage

grad_msel(
  par,
  control_lnL,
  out_type = "grad",
  n_sim = 1000L,
  n_cores = 1L,
  regularization = NULL
)
grad_msel(
  par,
  control_lnL,
  out_type = "grad",
  n_sim = 1000L,
  n_cores = 1L,
  regularization = NULL
)

Arguments

`par`	vector of parameters.
`control_lnL`	list with some additional parameters.
`out_type`	string representing the output type of the function.
`n_sim`	the number of random draws for multivariate normal probabilities.
`n_cores`	the number of cores to be used.
`regularization`	list of regularization parameters.

Log-likelihood Function of Multivariate Ordered Probit Model

Description

Calculates log-likelihood function of multivariate ordered probit model.

Usage

lnL_msel(
  par,
  control_lnL,
  out_type = "val",
  n_sim = 1000L,
  n_cores = 1L,
  regularization = NULL
)
lnL_msel(
  par,
  control_lnL,
  out_type = "val",
  n_sim = 1000L,
  n_cores = 1L,
  regularization = NULL
)

Arguments

`par`	vector of parameters.
`control_lnL`	list with some additional parameters.
`out_type`	string represeint the output type of the function.
`n_sim`	the number of random draws for multivariate normal probabilities.
`n_cores`	the number of cores to be used.
`regularization`	list of regularization parameters.

Extract Log-Likelihood from a Fit of the msel Function.

Description

Extract Log-Likelihood from a model fit of the msel function.

Usage

## S3 method for class 'msel'
logLik(object, ...)
## S3 method for class 'msel'
logLik(object, ...)

Arguments

`object`	object of class "msel"
`...`	further arguments (currently ignored)

Details

If estimator == "2step" in msel then function may return NA value since two-step estimator of covariance matrix may be not positively defined.

Value

Returns an object of class 'logLik'.

Leave-one-out cross-validation

Description

This function calculates root mean squared error (RMSE) for leave-one-out cross-validation of linear regression estimated via least squares method.

Usage

loocv(fit)
loocv(fit)

Arguments

fit

object of class lm.

Details

Fast analytical formula is used.

Value

This function returns a numeric value representing root mean squared error (RMSE) of leave-one-out cross-validation (LOOCV).

Examples

set.seed(123)
# Generate data according to linear regression
n <- 100
eps <- rnorm(n)
x <- runif(n)
y <- x + eps
# Estimate the model
model <- lm(y ~ x)
# Perform cross-validation
loocv(model)
set.seed(123)
# Generate data according to linear regression
n <- 100
eps <- rnorm(n)
x <- runif(n)
y <- x + eps
# Estimate the model
model <- lm(y ~ x)
# Perform cross-validation
loocv(model)

Likelihood ratio test

Description

This function performs chi-squared test for nested models.

Usage

lrtest_msel(model1, model2)
lrtest_msel(model1, model2)

Arguments

`model1`	the first model.
`model2`	the second model.

Details

Arguments model1 and model2 should be objects of class that has implementations of logLik and nobs methods. It is assumed that either model1 is nested into model2 or vice versa. More precisely it is assumed that the model with smaller log-likelihood value is nested into the model with greater log-likelihood value.

Arguments model1 and model2 may be the lists of models. If model1 is a list of models then it is assumed that the number of degrees of freedom and log-likelihood of the first model are just a sum of degrees of freedom and log-likelihoods of the models in this list. Similarly for model2.

If model1 or model2 is a list then the number of observations of the associated models are calculated as the sum of the numbers of observations of the models in corresponding lists. However sometimes it may be misleading. For example, when bivariate probit model (full) is tested against two independent probit models (restricted). Then it will be assumed that the number of observations in the restricted model is twice the number of observations in the full model that is not the case. Fortunately it will not affect the results of the likelihood ratio test.

Value

The function returns an object of class 'lrtest_msel' that is a list with the following elements:

n1 - the number of observations in the first model.
n2 - the number of observations in the second model.
ll1 - log-likelihood value of the first model.
ll2 - log-likelihood value of the second model.
df1 - the number of parameters in the first model.
df2 - the number of parameters in the second model.
restrictions - the number of restrictions in the nested model.
value - chi-squared (likelihood ratio) test statistic value.
p_value - p-value of the chi-squared (likelihood ratio) test.

Examples

set.seed(123)
# Generate data according to linear regression
n   <- 100
eps <- rnorm(n)
x1  <- runif(n)
x2  <- runif(n)
y   <- x1 + 0.2 * x2 + eps
# Estimate full model
model1 <- lm(y ~ x1 + x2)
# Estimate restricted (nested) model
model2 <- lm(y ~ x1)
# Likelihood ratio test results
lrtest_msel(model1, model2)
set.seed(123)
# Generate data according to linear regression
n   <- 100
eps <- rnorm(n)
x1  <- runif(n)
x2  <- runif(n)
y   <- x1 + 0.2 * x2 + eps
# Estimate full model
model1 <- lm(y ~ x1 + x2)
# Estimate restricted (nested) model
model2 <- lm(y ~ x1)
# Likelihood ratio test results
lrtest_msel(model1, model2)

Multivariate and multinomial sample selection and endogenous switching models with multiple outcomes.

Description

This function allows to estimate parameters of the multivariate and multinomial sample selection and endogenous switching models with multiple outcomes. Both maximum-likelihood and two-step estimators are implemented.

Usage

msel(
  formula = NA,
  formula2 = NA,
  formula3 = NA,
  data = NULL,
  groups = NA,
  groups2 = NA,
  groups3 = NA,
  marginal = list(),
  opt_type = "optim",
  opt_args = NA,
  start = NULL,
  estimator = "ml",
  cov_type = "mm",
  degrees = NA,
  degrees3 = NA,
  n_sim = 1000,
  n_cores = 1,
  control = list(),
  regularization = list(),
  type3 = "logit"
)
msel(
  formula = NA,
  formula2 = NA,
  formula3 = NA,
  data = NULL,
  groups = NA,
  groups2 = NA,
  groups3 = NA,
  marginal = list(),
  opt_type = "optim",
  opt_args = NA,
  start = NULL,
  estimator = "ml",
  cov_type = "mm",
  degrees = NA,
  degrees3 = NA,
  n_sim = 1000,
  n_cores = 1,
  control = list(),
  regularization = list(),
  type3 = "logit"
)

Arguments

`formula`	a list which i-th element is an object of class `"formula"` describing the form of the linear predictor (index) of the i-th ordinal equation. Mean and variance equations should be separated by the '\|' symbol.
`formula2`	a list which i-th element is an object of class `"formula"` describing the form of the linear predictor (index) of the i-th continuous equation.
`formula3`	an object of class `"formula"` describing the form of the linear predictor (index) of the multinomial equation.
`data`	a data frame containing the variables in the model.
`groups`	a matrix which (i, j)-th element is the j-th ordinal category (value starting from 0) of the i-th dependent ordinal variable. Each row of this matrix describes observable (in data) combination of categories - values of the ordinal dependent variables i.e., from the left hand side of `formula`. Special category `'-1'` means that variable in the j-th column is unobservable when other dependent variables have particular values i.e., given in the same row. See 'Details' for additional information.
`groups2`	the same as `groups` argument but for the continuous dependent variables from `formula2`. See 'Details' for additional information.
`groups3`	the same as `groups` argument but for the multinomial dependent variable from `formula3`. See 'Details' for additional information.
`marginal`	a list such that `marginal[[i]]` represents parameters of the marginal distribution of the random error of the `i`-th ordered equation and `names(marginal)[i]` is a name of this distribution. Marginal distributions are the same as in `pmnorm`.
`opt_type`	a character representing the optimization function to be used. If `opt_type = "optim"` then `optim` will be used. If `opt_type = "gena"` then `gena` will be applied i.e., a genetic algorithm. If `opt_type = "pso"` then `pso` will be used i.e., a particle swarm optimization.
`opt_args`	a list of input arguments for the optimization function selected via the `opt_type` argument. See 'Details' for additional information.
`start`	a numeric vector of initial parameters' values. It will be used as a starting point for the optimization purposes. It is also possible to provide an object of class `'msel'` then its `'par'` element will be used as a starting point.
`estimator`	a character determining estimation method. If `estimator = "ml"` then maximum-likelihood method will be used. If `estimator = "2step"` then two-step estimation procedure similar to Heckman's method will be applied. See 'Details' for additional information.
`cov_type`	a character determining the type of the covariance matrix estimate to be returned. First, suppose that `estimator = "ml"` then the following estimators are available. If `cov_type = "hessian"` then negative inverse of Hessian matrix will be applied. If `cov_type = "gop"` then inverse of Jacobian outer products will be used. If `cov_type = "sandwich"` then sandwich covariance matrix estimator will be applied. if `cov_type = "no"` then an identity matrix will be used. If `cov_type = "mm"` (default) then sandwich estimator is used along with the penalized log-likelihood function. Therefore if there is no regularization then `"mm"` and `"sandwhich"` estimators are the same. Second, suppose that `estimator = "2step"` then available estimators of the asymptotic covariance matrix are identity matrix `cov_type = "no"` and the method of moments `cov_type = "mm"`.
`degrees`	a vector of non-negative integers such that `degrees[i]` represents the degree of the polynomial which elements are selectivity terms associated with the `i`-th ordered equation. See 'Details' for additional information.
`degrees3`	a vector of non-negative integers such that `degrees3[i]` represents the degree of the polynomial which elements are selectivity terms associated with the `i`-th multinomial equation. See 'Details' for additional information.
`n_sim`	an integer representing the number of GHK draws when there are more than 3 ordered equations. Otherwise alternative (much more efficient) algorithms will be used to calculate multivariate normal probabilities.
`n_cores`	a positive integer representing the number of CPU cores used for the parallel computing. If possible it is highly recommend to set it equal to the number of available physical cores.
`control`	a list of control parameters. See 'Details' for additional information.
`regularization`	a list of control parameters for regularization. Element `ridge_ind` is a vector of indexes of parameters subject to regularization according to quadratic (ridge) penalty function. These indexes correspond to parameters from `par` output element. Set `show_ind` argument of `summary.msel` to `TRUE` to see these indexes. Element `ridge_scale` is a numeric vector of weights of the ridge penalty function. Element `ridge_location` is a numeric vector of values to be subtracted from the parameters before they pass into the penalty function. Elements `lasso_ind`, `lasso_scale` and `lasso_location` are the same but for the lasso penalty term.
`type3`	a character determining the type of the multinomial model to be considered. If `type3 = "logit"` then multinomial logit model will be used. If `type3 = "probit"` then multinomial probit model will be applied. See 'Details' for additional information.

Details

This function allows to estimate multivariate and multinomial sample selection and endogenous switching models with multiple outcomes. These models are the systems of ordinal, continuous and multinomial equations described by formula, formula2 and formula3 respectively.

Ordinal equations

Argument formula determines the regressors of the multivariate ordered probit model with the heteroscedastic random errors:

$z_{ji}^{*} = w_{ji}\gamma_{j} + \sigma_{ji}^{*}u_{ji},$

$\sigma_{ji}^{*} = \exp(w_{ji}^{*}\gamma_{j}^{*}), \quad u_{i}\sim N\left(\begin{bmatrix}0\\ \vdots\\ 0\end{bmatrix}, \Sigma\right), i.i.d.,$

$z_{ji}=\begin{cases} 0\text{, if }z_{ji}^{*}\leq c_{j1}\\ 1\text{, if }c_{j1}<z_{ji}^{*}\leq c_{j2}\\ 2\text{, if }c_{j2}<z_{ji}^{*}\leq c_{j3}\\ \vdots\\ m_{J-1}\text{, if }z_{(J-1)i}^{*}>c_{(J-1)m_{j-1}}\\ \end{cases},$

$z_{i}=(z_{1i},...,z_{Ji})^{T},\quad u_{i} = (u_{1i},u_{2i},...,u_{Ji})^{T},$

$i\in\{1,2,...,n\},\quad j\in\{1,2,...,J\},$

where:

$n$ - the number of observations. If there are no omitted observations then $n$ equals to nrow(data).
$J$ - the number of ordinal equations which equals to length(formula).
$z_{ji}^{*}$ - an unobservable (latent) value of the j-th dependent ordinal variable.
$z_{ji}$ - an observable (ordinal) value of the j-th dependent ordinal variable.
$m_{j}$ - the number of categories of $z_{ji}\in\{0,1,...,m_{j}\}$ .
$c_{jk}$ - the $k$ -th cut (threshold) of the j-th dependent ordinal variable.
$w_{ji}$ - the regressors of the $j$ -th mean equation which should be described in formula[[j]].
$\gamma_{j}$ - the regression coefficients of the $j$ -th mean equation.
$w_{ji}\gamma_{j}$ - the linear predictor (index) of the $j$ -th mean equation.
$u_{i}$ - a multivariate normal random vector which elements are standard normal random variables.
$\Sigma$ - a correlation matrix of $u_{i}$ so $\Sigma_{t_{1}t_{2}}=\text{corr}\left(u_{it_{1}}, u_{it_{2}}\right)$ .
$\sigma_{ji}^{*}$ - a heteroscedastic standard deviation.
$\sigma_{ji}^{*}u_{ji}$ - the heteroscedastic random errors.
$w_{ji}^{*}$ - the regressors of the $j$ -th variance equation which should be described in formula[[j]] after '|' symbol.
$\gamma_{j}^{*}$ - the regression coefficients of the $j$ -th variance equation.
$w_{ji}^{*}\gamma_{j}^{*}$ - the linear predictor (index) of the $j$ -th variance equation.

Constant terms (intercepts) are excluded from the model for identification purposes. If $z_{ji}$ is a binary variable then $-c_{j1}$ may be interpreted as a constant term of the $j$ -th ordinal equation. If all $z_{ji}$ are binary variables then the model becomes a multivariate probit.

By default the joint distribution of $u_{i}$ is multivariate normal. However by using marginal argument it is possible to consider the joint distribution that is determined by the Gaussian copula and possibly non-normal marginal distributions. Specifically, names(marginal)[i] is the name of the marginal distribution of $u_{ji}$ and marginal[[i]] is the number of parameters of this distribution. The marginal distributions are the same as in pmnorm.

Multinomial equation

Argument formula3 determines the regressors of the multinomial equation:

$\tilde{z}_{ji}^{*} = \tilde{w}_{i}\tilde{\gamma}_{j} + \tilde{u}_{ji}, \qquad j\in\{0,1,...,\tilde{J} - 1\},$

$\tilde{z}_{i}=\underset{t\in\{0,...,\tilde{J}-1\}}{\text{argmax}} \text{ }\tilde{z}_{ti}^{*}, \qquad \tilde{u}_{i} = (\tilde{u}_{0i},\tilde{u}_{1i},..., \tilde{u}_{(\tilde{J}-1)i})^{T},$

where:

$\tilde{J}$ - the number of the alternatives i.e., possible values of the dependent variable of the multinomial equation.
$\tilde{z}_{ji}^{*}$ - an unobservable (latent) value of the j-th alternative. Usually $\tilde{z}_{ji}^{*}$ is interpreted as a utility of the j-th alternative.
$\tilde{z}_{i}$ - a selected alternative i.e., one which provides the greatest utility $\tilde{z}_{ji}^{*}$ .
$\tilde{w}_{i}$ - the regressors of the multinomial equation which should be described in formula3 and assumed to be the same for all the alternatives.
$\tilde{\gamma}_{j}$ - the regression coefficients of the $j$ -th alternative's equation.
$\tilde{w}_{i}\tilde{\gamma}_{j}$ - the linear predictor (index) of the $j$ -th alternative's equation.
$\tilde{u}_{i}$ - a vector of random errors.

For the identification purposes it is assumed that the regression coefficients of the last alternative are zero $\tilde{\gamma}_{\tilde{J} - 1} = (0,...,0)^{T}$ .

Joint distribution of $\tilde{u}_{i}$ depends on the value of type3 argument. If type3 = "logit" then multinomial logit model is considered. It assumes that $\tilde{u}_{ji}$ are independent and their marginal distribution is Gumbel (error value distribution). If type3 = "probit" then multinomial probit model is used so it is assumed that joint distribution of $\tilde{u}_{i}$ is multivariate normal with zero mean and the covariance matrix $\tilde{\Sigma}$ . For identification purposes it is also assumed that $\tilde{\Sigma}_{11} = 1$ so $Var(\tilde{u}_{0i}) = 1$ . In addition $\tilde{u}_{(\tilde{J}-1)i}=0$ which implies $\tilde{\Sigma}_{t(\tilde{J})}=\tilde{\Sigma}_{(\tilde{J})t}=0$ for all $t\in\{0,...,\tilde{J}-1\}$ .

Continuous equations

Argument formula3 determines the regressors of the continuous equations:

$y_{r^{(v)}i}^{(v)} = x_{i}^{(v)}\beta_{r^{(v)}}^{(v)} + \varepsilon_{r^{(v)}i}^{(v)},$

$r^{(v)}\in\{0,1,...,R^{(v)} - 1\}, \quad v\in \{1,...,V\},$

where:

$V$ - the number of continuous equations.
$r^{(v)}$ - the regime of the $v$ -th continuous equation.
$y_{r^{(v)}i}^{(v)}$ - the $r^{(v)}$ -th potential outcome (in a sense of the Neyman-Rubin framework) of the $v$ -th continuous equation.
$R^{(v)}$ - the number of the potential outcomes of the $v$ -th continuous equation.
$x_{i}^{(v)}$ - the regressors of the $v$ -th continuous equation. They are provided via formula2[[v]].
$\beta_{r^{(v)}}^{(v)}$ - regression coefficients of the $v$ -th continuous equation in the regime $r^{(v)}$ .
$\varepsilon_{r^{(v)}i}^{(v)}$ - a random error of the $v$ -th continuous equation in the regime $r^{(v)}$ .

Estimation of the model with multivariate ordinal and multinomial equations

If formula2 is not provided then maximum-likelihood estimator is used estimator = "ml" to estimate the parameters of the multivariate ordinal and multinomial equations.

If both formula and formula3 are provided then parameters of the multivariate ordered and multinomial equations are estimated under the assumption that $u_{i}$ is independent of $\tilde{u}_{i}$ . Therefore the estimates are identical to those obtained by separate estimation of these models. We may relax the independence assumption during future updates.

Estimation of the model with continuous equations

If formula and formula3 are not provided then it is assumed that each continuous equation has only one regime so $R^{(v)} = 1$ for each $v\in\{1,...,V\}$ .

If estimator = "ml" then maximum-likelihood estimator is used under the assumption that the distribution of random errors is multivariate normal. If estimator = "2step" then $V$ -stage least squares estimator is used. In the latter case $v$ -th equation is estimated by the least squares estimator and for every $v_{0}$ such that $v_{0} < v$ if $y_{0i}^{(v_{0})}$ is included in $x_{i}^{(v)}$ then $y_{0i}^{(v_{0})}$ is substituted with its estimate $\hat{y}_{0i}^{(v_{0})}$ obtained on the $v_{0}$ -th step. Therefore if $v_{0} < v$ then if some endogenous variable appears on the left hand side of formula2[[v]] it should not appear on the right hand side of formula2[[v0]].

Estimation of the model with ordinal outcomes and multivariate ordered selection

Suppose that $z_{i}$ represents the ordinal potential outcomes while the observable values are $z_{i}^{(o)} = g^{(1)}(z_{i})$ , where function $g^{(1)}(z_{i})$ converts unobservable values of $z_{i}$ to $-1$ . Therefore $z_{ji}^{(o)}$ instead of $z_{i}$ appears on the left hand side of the formula[[j]].

For example, consider a binary variable for the employment status (0 - unemployed, 1 - employed) and an ordinal variable $z_{2i}$ (ranging from 0 to 2) for job satisfaction (0 - unsatisfied, 1- satisfied, 2 - highly satisfied). Then $z_{2i}$ is observable only when $z_{1i}$ equals 1 since job satisfaction observable only for working individuals. Consequently $z_{2i}^{(o)}$ should be equal to -1 (minus one) whenever $z_{1i}$ equals to 0:

$z_{i}^{(o)}=g^{(1)}(z_{i})= \begin{cases} (1, 2)\text{, if }z_{i} = (1, 2)\\ (1, 1)\text{, if }z_{i} = (1, 1)\\ (1, 0)\text{, if }z_{i} = (1, 0)\\ (0, -1)\text{, otherwise}\\ \end{cases}$

Rows of the matrix groups determine all possible values of the function $g^{(1)}(z_{i})$ . In this particular example matrix groups should have the following form:

$\text{groups} = \begin{bmatrix} 1 & 2\\ 1 & 1\\ 1 & 0\\ 0 & -1 \end{bmatrix}.$

Notice that in this particular example it is necessary to ensure that in data if $z_{1i}^{(o)}$ equals 0 then $z_{2i}^{(o)}$ equals to -1. Also in this case matrix groups will be created automatically so there is no need to provide it manually.

By using groups argument it is straightforward to consider various other models with ordinal outcomes and multivariate ordered selection mechanisms.

If some values of the ordinal variables $z_{ji}$ are missing (by random) i.e., take NA value then the contribution of other ordinal dependent variables (for the i-th observation) still may be included into the likelihood function by manually substituting NA with -1 in the data. However, ensure that this particular (missing) $z_{ji}$ is not a regressor for other dependent variable that may happen in the hierarchical systems.

It is useful to use groups argument to consider causal inference models. For example, suppose that $z_{1i}$ represents a binary treatment variable for the university diploma (0 - no diploma, 1 - has diploma). Also, $z_{2i}$ is a binary potential outcome for the employment status (0 - unemployed, 1 - employed) of the individual if she has university diploma. Finally, $z_{3i}$ is a binary potential outcome for the employment status (0 - unemployed, 1 - employed) if individual does not have university diploma. Since $z_{2i}$ is observable only if $z_{1i} = 1$ and $z_{3i}$ is observable only when $z_{2i} = 0$ we get:

$z_{i}^{(o)} = \begin{cases} (1, 1, -1)\text{, if }z_{1i} = 1\text{ and }z_{2i}=1\\ (1, 0, -1)\text{, if }z_{1i} = 1\text{ and }z_{2i}=0\\ (0, -1, 1)\text{, if }z_{1i} = 0\text{ and }z_{3i}=1\\ (0, -1, 0)\text{, if }z_{1i} = 0\text{ and }z_{3i}=0 \end{cases}.$

Therefore to estimate this model it is necessary to ensure that in data we have $z_{2i}^{(o)} = -1$ when $z_{1i}^{(o)} = 0$ and $z_{3i}^{(o)} = -1$ if $z_{1i}^{(o)} = 1$ . Also the groups argument should include all possible values of $z_{i}^{(o)}$ :

$\text{groups} = \begin{bmatrix} 1 & 1 & -1\\ 1 & 0 & -1\\ 0 & -1 & 1\\ 0 & -1 & 0 \end{bmatrix}.$

Estimation of the model with continuous outcomes and multivariate ordered selection

To simplify the notations suppose that there is only one continuous equation with multiple regimes:

$y_{ri} = x_{i}\beta_{r} + \varepsilon_{ri},$

$y_{i} = \begin{cases} \text{unobservable, if }g^{(2)}\left(z_{i}^{(o)}\right) = -1\\ y_{0i}\text{, if }g^{(2)}\left(z_{i}^{(o)}\right) = 0\\ y_{1i}\text{, if }g^{(2)}\left(z_{i}^{(o)}\right) = 1\\ \vdots\\ y_{(R^{*}-1)i}\text{, if }g^{(2)}\left(z_{i}^{(o)}\right) = R^{*} - 1\\ \end{cases},$

$\varepsilon_{i} = (\varepsilon_{0i},\varepsilon_{1i},...,\varepsilon_{(R^{*}-1)i}),\quad r\in\{0,1,...,R^{*} - 1\},$

where:

$y_{i}$ - an observable continuous outcome.
$r$ - the index of the potential outcome.
$R^{*}$ - the number of the regimes.
$y_{ri}$ - a continuous potential outcome i.e., the value of $y_{i}$ in the regime $r$ .
$x_{i}$ - the vector of regressors provided in formula2.
$\beta_{r}$ - the regression coefficients in the $r$ -th regime.
$g^{(2)}(z_{i}^{(o)})$ - a function determining which potential outcome is observable depending on the observable values of the ordinal variables $z_{i}^{(o)}$ .
$\varepsilon_{i}$ - a vector of random errors.

The value of groups2[i] argument specifies the value of $g^{(2)}(z_{i}^{(o)})$ when $z_{i}^{(o)}$ equals to groups[i, ]. The values of $y_{i}$ in data such that $g^{(2)}(z_{i}^{(o)})=-1$ should be set to NA.

For example, consider a system of equations for wage $y_{i}$ , employment status $z_{1i}$ (0 - unemployed, 1 - employed) and job satisfaction $z_{2i}$ (0 - unsatisfied, 1- satisfied, 2 - highly satisfied). Notice that wage and job satisfaction are observable only for working individuals. Also suppose that wage is unobservable for unsatisfied workers and observable in different regimes for other workers. Namely, for satisfied workers $z_{2i} = 1$ we observe $y_{0i}$ and for highly satisfied workers $z_{2i} = 2$ we observe $y_{1i}$ .

To estimate this model it is necessary to manually specify the structure of the equations via groups and groups2 arguments by providing all possible combinations of the ordinal variables and the regimes of the continuous equation:

$\text{groups} = \begin{bmatrix} 1 & 2\\ 1 & 1\\ 1 & 0\\ 0 & -1 \end{bmatrix}, \text{groups2} = \begin{bmatrix} 1\\ 0\\ -1\\ -1 \end{bmatrix}.$

Notice that groups2[1] = 1 indicates that when groups[1, ] = c(1, 2) i.e. $z_{1i} = 1$ and $z_{2i} = 2$ we observe $y_{i}$ in regime 1 corresponding to the wage of highly satisfied workers $y_{1i}$ . Similarly groups2[2] = 0 indicates that when groups[2, ] = c(1, 1) i.e., $z_{1i} = 1$ and $z_{2i} = 1$ we observe $y_{i}$ in the regime 0 corresponding to the wage of the satisfied workers $y_{0i}$ . Also, groups3[3] = -1 means that when groups[3, ] = c(1, 0) i.e., $z_{1i} = 1$ and $z_{2i} = 0$ we do not observe the wage of the worker $y_{i}$ since he is unsatisfied. Finally, groups3[4] = -1 means that when groups[4, ] = c(0, -1) i.e., $z_{1i} = 0$ and $z_{2i}^{(o)} = -1$ we do not observe the wage of the worker $y_{i}$ since he is unemployed.

If the joint distribution of $\varepsilon_{i}$ and $u_{i}$ is multivariate normal then according to Kossova and Potanin (2018):

$y_{ri}=x_{i}\beta_{r}+\sum\limits_{j=1}^{J} \Sigma^{(12)}_{j(r+1)}\lambda_{ji}+\varepsilon_{ri}^{*},$

where:

$\varepsilon_{ri}^{*} = \varepsilon_{ri} - E(\varepsilon_{i}|z_{1i},...z_{Ji})=\varepsilon_{ri} - \sum\limits_{j=1}^{J} \Sigma^{(12)}_{j(r+1)}\lambda_{ji},$

$\lambda_{ji}= \lambda_{ji}^{(1)} + \lambda_{ji}^{(2)},\qquad \Sigma^{(12)}_{j(r+1)} = cov(u_{ji}, \varepsilon_{r+1}),$

$\lambda_{ji}^{(1)}= \begin{cases} 0\text{, if }z_{ji} = 0\\ -\frac{\partial \ln P_{i}^{*}}{\partial a_{ji}}\text{, otherwise } \end{cases}, \qquad \lambda_{ji}^{(2)}= \begin{cases} 0\text{, if }z_{ji} = m_{j} - 1\\ -\frac{\partial \ln P_{i}^{*}}{\partial b_{ji}}\text{, otherwise } \end{cases},$

$P_{i}^{*}(a_{1i},...,a_{Ji};b_{1i},...,b_{Ji})= P(a_{1i}\leq u_{1i}\leq b_{1i},....,a_{Ji}\leq u_{Ji}\leq b_{Ji}),$

$a_{ji}= \begin{cases} -\infty\text{, if }z_{ji} = 0\\ \frac{c_{jz_{ji}}-w_{ji}\gamma_{j}}{\sigma_{ji}^{*}}\text{, otherwise } \end{cases}, \qquad b_{ji}= \begin{cases} \infty\text{, if }z_{ji} = m_{j} - 1\\ \frac{c_{j(z_{ji}+1)}-w_{ji}\gamma_{j}}{\sigma_{ji}^{*}}\text{, otherwise } \end{cases}.$

Notice that the regression equation has selectivity terms $\lambda_{ji}$ which may be correlated with $x_{i}$ . Therefore until random errors $u_{i}$ and $\varepsilon_{i}$ are correlated the least squares estimator of $x_{i}$ on $y_{ri}$ is inconsistent. To get consistent estimates of $\beta_{r}$ it is possible to use maximum-likelihood estimator = "ml" or two-step (method of moments) estimator = "2step" estimator.

If the two-step estimator is used then on the first step $\lambda_{ji}$ are estimated as the functions of the estimates of the multinomial heteroscedastic ordered probit model. On the second step least squares regression of $y_{ri}$ on $x_{i}$ and the first step estimates $\hat{\lambda}_{ji}$ is used to estimate $\beta_{r}$ and $\Sigma^{(12)}_{j(r+1)}$ .

If the joint distribution of random errors $\varepsilon_{i}$ , $u_{i}$ is not multivariate normal then $\lambda_{ji}$ terms may enter continuous equation non-linearly. Following Newey (2009) and E. Kossova, L. Kupriianova, and B. Potanin (2020) it is assumed that:

$y_{ri}= x_{i}\beta_{r} + \zeta_{i}\tau_{r} + \varepsilon_{i}^{*},$

where $\tau_{r}$ is a $n_{\lambda}$ -dimensional column vector and:

$\zeta_{i} = (\zeta_{1}(\lambda_{i}),..., \zeta_{n_{\lambda}}(\lambda_{i})),\qquad \lambda_{i} = (\lambda_{1i},..., \lambda_{Ji}).$

Functions $\zeta_{t}(\lambda_{i})$ are specified manually by the user in the formula2 inside I(). For example, to specify $\zeta_{t}(\lambda_{i}) = \lambda_{1i}\times \lambda_{2i}$ it is sufficient to have a term I(lambda1 * lambda2) in formula2. Notice that to avoid the confusions no variables in data should have the names containing "lambda". Otherwise these variables will be dropped.

It is possible to specify $\zeta_{t}(\lambda_{i})$ functions as the polynomial without interaction terms by using degrees argument. Specifically, if degrees[j] = t then lambdaj, I(lambdaj ^ 2),...,I(lambdaj ^ t) terms are added to formula2. However, if degrees argument is used then no functions of lambdaj should be provided manually in formula2. Otherwise it will be assumed that degrees is a vector of zeros. Also if estimator = "2step", there is not lambdaj terms in formula2 and degrees is NA then degrees will be converted in a vector of ones.

If there are multiple continuous equations then formula2 should be a list of formulas. Further, if estimator = "2step" then the second step is a $V$ -stage least squares estimator with lambda terms. If they are provided via degrees argument then it should be a matrix which v-th row corresponds to the v-th continuous equation.

For example, consider previous example with additional continuous equation for working hours $y_{i}^{(2)}$ which does not vary with the satisfaction of the workers $z_{2i}$ but observable only for the employed individuals $z_{1i} = 1$ . To estimate the system with this additional continuous equation simply substitute all $y_{i}^{(2)}$ (such that $z_{1i}=0$ ) in data with NA and specify:

$\text{groups} = \begin{bmatrix} 1 & 2\\ 1 & 1\\ 1 & 0\\ 0 & -1 \end{bmatrix}, \text{groups2} = \begin{bmatrix} 1 & 0\\ 0 & 0\\ -1 & 0\\ -1 & -1 \end{bmatrix}.$

Notice that groups2[, 1] describes the regimes of the wage equation $y_{i}^{(1)}$ while groups[, 2] contains the regimes of the hours equation $y_{i}^{(2)}$ . Note that formula of the first equation (wage) should be specified in formula2[[1]] and formula of the second equation should be provided via formula2[[2]] i.e., as the first and the second elements in a formula2 list correspondingly.

If marginal argument is used then aforementioned formula of $\lambda_{ji}$ is slightly modified to address for the non-normal marginal distribution of $u_{ji}$ .

Estimation of the model with continuous outcomes and multinomial selection

The only difference with the previous model is that the observable value of the continuous equation depends on the value of the multinomial equation described in formula3. Therefore $g^{(2)}(z_{i}^{(o)})$ is substituted with $g^{(3)}(\tilde{z}_{i})$ . Also groups3 argument instead of groups is used. Since there is only a single multinomial equation argument groups3 is a vector. If groups3[k] = q and groups2[k, v] = r then $\tilde{z}_{i} = q$ implies $y_{i}^{(v)} = y_{ri}^{(v)}$ . Remind that a special value r = -1 implies that $y_{i}^{(v)}$ is unobservable.

Only two-step estimator = "2step" estimator of this model is available that is similar to the one described above. The only difference is the formula used to estimate selectivity terms. If type = "logit" then two-step estimator of Dubin-McFadden (1984) is used so selectivity terms are as follows:

$\lambda_{ji} = \begin{cases} -\ln P(\tilde{z}_{i} = j)\text{, if }\tilde{z}_{i} = j\\ \frac{P(\tilde{z}_{i} = j)\ln P(\tilde{z}_{i} = j)}{1 - P(\tilde{z}_{i} = j)}\text{, otherwise} \end{cases},$

where $j\in\{0,...,\tilde{J} - 1\}$ and:

$P(\tilde{z}_{i} = j) = \begin{cases} \frac{e^{(\tilde{w}_{i}\tilde{\gamma}_{j})}}{1 + \sum\limits_{q=0}^{\tilde{J} - 2}e^{(\tilde{w}_{i}\tilde{\gamma}_{q})}} \text{, if }j\ne \tilde{J} - 1\\ \frac{1}{1 + \sum\limits_{q=0}^{\tilde{J} - 2}e^{(\tilde{w}_{i}\tilde{\gamma}_{q})}} \text{, otherwise}\\ \end{cases}.$

If type = "probit" then two-step estimator of Kossova and Potanin (2022) is used with the selectivity terms of the form:

$\lambda_{ji} = \left(A^{(\tilde{z}_{i})}\lambda_{i}^{*}\right)_{j},$

where $j\in\{0,...,\tilde{J} - 2\}$ and:

$A^{(j)}_{t_{1}t_{2}} = \begin{cases} 1\text{, if }t_{1} = j + 1\\ -1\text{, if }t_{1} < j + 1\text{ and }t_{1} = t_{2}\\ -1\text{, if }t_{1} > j + 1\text{ and }t_{1} = t_{2} + 1\\ 0\text{, otherwise } \end{cases},$

$t_{1},t_{2}\in\{1,...,\tilde{J} - 1\},$

$\lambda_{i}^{*} = \nabla \ln P\left(\tilde{z}_{i}\right) = \nabla \ln F_{\tilde{u}^{(ji)}}\left(\tilde{z}_{1}^{(ji)},..., \tilde{z}_{\tilde{J}-1}^{(ji)}\right)= \left(\lambda_{1i}^{*},...,\lambda_{(\tilde{J}-1)i}^{*} \right)^{T},$

$\tilde{u}^{(ji)} = \left(\tilde{u}_{0i}-\tilde{u}_{ji},\tilde{u}_{1i}-\tilde{u}_{ji},..., \tilde{u}_{(j-1)i}-\tilde{u}_{ji},\tilde{u}_{(j+1)i}-\tilde{u}_{ji},..., \tilde{u}_{(\tilde{J} - 1)i}-\tilde{u}_{ji}\right),$

$\tilde{z}^{(ji)} = \tilde{w}_{i}\left( \left(\tilde{\gamma}_{j}-\tilde{\gamma}_{0}\right), \left(\tilde{\gamma}_{j}-\tilde{\gamma}_{1}\right),..., \left(\tilde{\gamma}_{j}-\tilde{\gamma}_{j-1}\right), \left(\tilde{\gamma}_{j}-\tilde{\gamma}_{j+1}\right),..., \left(\tilde{\gamma}_{j}-\tilde{\gamma}_{\tilde{J} - 1}\right)\right).$

Probabilities $P(z_{i})$ are calculated by using a cumulative distribution function of the multivariate normal distribution with zero mean and the covariance matrix of $\tilde{u}^{(ji)}$ .

In formula2 selectivity terms associated with the multinomial equation should be named lambdaj_mn instead of lambdaj. Argument degrees3 is similar to degrees.

Consider a simple example of this model. Suppose that $\tilde{z}_{i}$ is a multinomial variable for the employment status (0 - unemployed, 1 - working in IT sector, 2 - working in other sector). Wage $y_{i}$ is unobservable for unemployed $\tilde{z}_{i} = 0$ , equals to $y_{0i}$ in IT sector $\tilde{z}_{i} = 1$ and equals to $y_{1i}$ in other sectors $\tilde{z}_{i} = 2$ . To estimate this model set groups3 = (0, 1, 2) and groups2 = (-1, 0, 1). Then substitute all $y_{i}$ such that $\tilde{z}_{i} = 0$ with NA.

Estimation of the model with continuous outcomes and mixed selection

It is possible to consider the model with continuous outcomes and both multinomial and ordinal selection equations. Remind that it is assumed that random errors of the ordered and multinomial equations are independent. Therefore if formula and formula3 are provided then both lambdaj and lambdaj_mn are included in formula2. Only two-step estimator estimator = "2step" is available for this model.

Missing values

If any of the left hand side variables (regressors) of formula[[j]] is missing then the right hand side variable of formula[[j]] will be set to NA in data. Similar is true for formula2 and formula3.

Additional information

Functions pmnorm and dmnorm are used internally for calculation of multivariate normal probabilities, densities and their derivatives.

Currently control has no input arguments intended for the users. This argument is used for some internal purposes of the package.

Optimization always starts with optim. If opt_type = "gena" or opt_type = "pso" then gena or pso is used to proceed optimization starting from initial point provided by optim. Manual arguments to optim should be provided in a form of a list through opt_args$optim. Similarly opt_args$gena and opt_args$pso provide manual arguments to gena and pso. For example to provide Nelder-Mead optimizer to optim and restrict the number of genetic algorithm iterations to $10$ make opt_args = list(optim = list(method = "Nelder-Mead"), gena = list(maxiter = 10)).

If estimator = "2step" then it is possible to precalculate the first step model with msel function and pass it through the formula argument. It allows to experiment with various formula2 and degrees specifications without extra computational burden associated with the first step estimation.

If estimator = "2step" then the method of moments estimator of the asymptotic covariance matrix is used as described in Meijer and Wansbeek (2007).

Value

This function returns an object of class "msel". It is a list which may contain the following elements:

par - a vector of the estimates of the parameters.
estimator - the same as the estimator input argument.
type3 - the same as the type3 input argument.
formula - the same as formula input argument but all elements are coerced to "formula" type.
formula2 - the same as formula2 input argument but all elements are coerced to "formula" type.
formula3 - the same as formula3 input argument but all elements are coerced to "formula" type.
model1 - an object of class "msel" with the first step estimation results.
data - the same as data input argument but without missing (by random) values.
W_mean - a list such that W_mean[[j]] is a matrix of the regressors $w_{ji}$ of the mean equation of $z_{ji}^{*}$ .
W_var - a list such that W_var[[j]] is a matrix of the regressors $w_{ji}^{*}$ of the variance equation of $z_{ji}^{*}$ .
X - a list such that X[[v]] is a numeric matrix of regressors $x_{i}^{(v)}$ of the v-th continuous equation $y_{i}^{(v)}$ .
W_mn - a matrix of the regressors $\tilde{w}_{i}$ of the multinomial equation of $\tilde{z}_{ji}^{*}$ .
dependent - a numeric matrix which $j$ -th column dependent[, j] is a vector of the ordinal dependent variable $z_{ji}^{(o)}$ values.
dependent2 - a numeric matrix which $v$ -th column dependent2[, v] is a vector of the continuous dependent variable $y_{i}^{(v)}$ values.
dependent3 - a numeric vector of values of the multinomial dependent variable $\tilde{z}_{j}$ .
groups - the same as groups input argument or automatically generated matrix representing the structure of the system of equations. Please, see 'Details' section above for more information.
groups2 - the same as groups2 input argument or automatically generated matrix representing the structure of the system of equations. Please, see 'Details' section above for more information.
groups3 - the same as groups3 input argument or automatically generated matrix representing the structure of the system of equations. Please, see 'Details' section above for more information.
marginal - the same as marginal input argument.
ind - a list containing some indexes partition of the model (not intended for the users).
start - the same as the start input argument.
twostep - a list such that twostep[[v]][[r + 1]] is an object of class "lm" associated with the second step estimates of the v-th equation in the regime r.
y_pred - a numeric matrix such that y_pred[, v] is a second step prediction of the $y_{i}^{(v)}$ .
coef - a list which $j$ -th element coef[[j]] is a numeric vector representing $\hat{\gamma}_{j}$ .
coef_var - a list which $j$ -th element coef_var[[j]] is a numeric vector representing $\hat{\gamma}_{j}^{*}$ .
cuts - a list which $j$ -th element cuts[[j]] is a numeric vector representing $\hat{c}_{j}$ .
coef2 - a list of numeric matrices such that coef2[[v]][, r + 1] is a numeric vector representing $\hat{\beta}_{r^{(v)}}^{(v)}$ .
sigma - a numeric matrix such that sigma[j, t] is a numeric value representing $\widehat{Cov}(u_{ji}, u_{ti})$ .
var2 - a list of numeric vectors such that var2[[v]][r + 1] represents $\widehat{Var}(\varepsilon_{ri}^{(v)})$ .
cov2 - a list of numeric matrices which element sigma2[[v]][j, r + 1] represents $\widehat{Cov}(u_{ji}, \varepsilon_{ri}^{(v)}))$ .
sigma2 - a list of numeric matrices representing the estimates of the covariances between random errors of the continuous equations in different regimes $\widehat{Cov}(\varepsilon_{r^{(v)}i}^{(v)}, \varepsilon_{r^{(t)}i}^{(t)})$ .
marginal_par - a list such that marginal_par[[j]] is a numeric vector of estimates of the parameters of the marginal distribution of $u_{ji}$ .
coef3 - a numeric matrix such that coef3[j + 1, ] is a numerc vector representing $\hat{\tilde{\gamma}}_{j}$ .
sigma3 - a numeric matrix such that sigma3[j + 1, t + 1] is a numeric value representing $\widehat{Cov}(\tilde{u}_{ji}, \tilde{u}_{ti})$ .
lambda - a numeric matrix such that lambda[i, j] corresponds to $\hat{\lambda}_{ji}$ of the ordinal equations.
lambda_mn - a numeric matrix such that lambda_mn[i, j] corresponds to $\hat{\lambda}_{ji}$ of the multinomial equation.
H - if estimator = "ml" then H is a Hessian matrix of the log-likelihood function. If estimator = "2step" then H is a numeric matrix of the derivatives of mean sample scores respect to the estimated parameters.
J - if estimator = "ml" then J is a Jacobian matrix of the log-likelihood function. If estimator = "2step" then J is a numeric matrix such that J[, s] is a vector of sample scores associated with the parameter par[s].
cov_type - the same as cov_type input argument.
cov - an estimate of the asymptotic covariance matrix of the parameters' estimator.
tbl - a list of special tables used to create a summary (not intended for the users).
se - a numeric vector of standard errors of the estimates.
p_value - a numeric vectors of the p-values of the tests on the significance of the estimated parameters where the null hypothesis is that corresponding parameter is zero.
logLik - the value of log-likelihood function at par.
other - a list of additional variables not intended for the users.

It is highly recommended to get the estimates of the parameters via coef.msel function.

References

W. K. Newey (2009). Two-step series estimation of sample selection models. The Econometrics Journal, vol. 12(1), pages 217-229.

E. Kossova, B. Potanin (2018). Heckman method and switching regression model multivariate generalization. Applied Econometrics, vol. 50, pages 114-143.

E. Kossova, L. Kupriianova, B. Potanin (2020). Parametric and semiparametric multivariate sample selection models estimators' accuracy: Comparative analysis on simulated data. Applied Econometrics, vol. 57, pages 119-139.

E. Kossova, B. Potanin (2022). Estimation of Gaussian multinomial endogenous switching model. Applied Econometrics, vol. 67, pages 121-143.

E. Meijer, T. Wansbeek (2007). The sample selection model from a method of moments perspecrive. Econometric Reviews, vol. 26(1), pages 25-51.

Examples


# ---------------------------------------
# CPS data example
# ---------------------------------------

# Set seed for reproducibility
set.seed(123)

# Upload data
data(cps)

# Prepare the variable for education
cps$educ                    <- NA
cps$educ[cps$basic == 1]    <- 0
cps$educ[cps$bachelor == 1] <- 1
cps$educ[cps$master == 1]   <- 2

# Labor supply (probit) model
f_work <- work ~ age + I(age ^ 2) + bachelor + master + health + 
                 slwage + nchild
model1 <- msel(f_work, data = cps)
summary(model1)
    
# Education choice (ordered probit) model
f_educ <- educ ~ age + I(age ^ 2) + sbachelor + smaster
model2 <- msel(f_educ, data = cps)
summary(model2)   

# Education choice (multinomial logit) model
model3 <- msel(formula3 = f_educ, data = cps, type3 = "logit")
summary(model3)  

# Education choice (multinomial probit) model
model4 <- msel(formula3 = f_educ, data = cps, type3 = "probit")
summary(model4) 

# Labor supply with endogenous ordinal education 
# treatment (recursive or hierarchical ordered probit) model
model5 <- msel(list(f_work, f_educ), data = cps)  
summary(model5)         

# Sample selection (on employment) Heckman's model
f_lwage                  <- lwage ~ age + I(age ^ 2) + 
                                    bachelor + master + health
model6                   <- msel(f_work, f_lwage, data = cps)
summary(model6)

# Ordinal endogenous education treatment with non-random 
# sample selection into employment
model7 <- msel(list(f_work, f_educ), f_lwage, data = cps)
summary(model7)
 
# Ordinal endogenous switching on education model with 
# non-random selection into employment
groups   <- cbind(c(1, 1, 1, 0, 0, 0),
                  c(0, 1, 2, 0, 1, 2))
groups2  <- matrix(c(0, 1, 2, -1, -1, -1), ncol = 1)
f_lwage2 <- lwage ~ age + I(age ^ 2) + health
model8   <- msel(list(f_work, f_educ), f_lwage2,
                      groups = groups, groups2 = groups2,
                      data   = cps)
summary(model8)

# Multinomial endogenous switching on education model with 
# non-random selection into employment
groups  <- matrix(c(1, 1, 1, 0, 0, 0), ncol = 1)
groups2 <- matrix(c(0, 1, 2, -1, -1, -1), ncol = 1)
groups3 <- c(0, 1, 2, 0, 1, 2)
model9  <- msel(f_work, f_lwage2, f_educ,
                groups    = groups,  groups2 = groups2, 
                groups3   = groups3, data    = cps,
                estimator = "2step")
summary(model9)


# ---------------------------------------
# Simulated data example 1
# Ordered probit and other univariate
# ordered choice models
# --------------------------------------

# ---
# Step 1
# Simulation of the data
# ---

# Load required package
library("mnorm")

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)

# Random errors
u <- rnorm(n = n, mean = 0, sd = 1)

# Coefficients
gamma <- c(-1, 2)

# Linear predictor (index)
li <- gamma[1] * w1 + gamma[2] * w2

# Latent variable
z_star <- li + u

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordinal outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Data
data <- data.frame(w1 = w1, w2 = w2, z = z)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(z ~ w1 + w2, data = data)
summary(model)

# Compare the estimates and true values of the parameters
  # regression coefficients
gamma_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma, estimate = gamma_est)
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   

# ---
# Step 3
# Estimation of the probabilities and marginal effects
# ---

# Predict conditional probability of the dependent variable
# equals to 2 for every observation in a sample.
# P(z = 2 | w)
prob <- predict(model, group = 2, type = "prob")
head(prob)

# Calculate mean marginal effect of w2 on P(z = 1 | w)
mean(predict(model, group = 1, type = "prob", me = "w2"))

# Calculate probabilities and marginal effects
# for manually provided observations.
  # new data
newdata <- data.frame(z = c(1, 1), 
                      w1 = c(0.5, 0.2), 
                      w2 = c(-0.3, 0.8))
  # probability P(z = 2 | w)
predict(model, group = 2, type = "prob", newdata = newdata)
  # linear predictor (index)
predict(model, type = "li", newdata = newdata)  
  # marginal effect of w1 on P(z = 2 | w)
predict(model, group = 2, type = "prob", newdata = newdata, me = "w1")
  # marginal effect of w1 and w2 on P(z = 3 | w)
predict(model, group   = 3,       type = "prob", 
               newdata = newdata, me   = c("w1", "w2"))
  # marginal effect of w2 on the linear predictor (index)
predict(model, group = 2, type = "li", newdata = newdata, me = "w2")
  # discrete marginal effect:
  # P(z = 2 | w1 = 0.5, w2) - P(z = 2 | w1 = 0.2, w2)
predict(model, group = 2,    type = "prob", newdata = newdata, 
               me    = "w2", eps  = c(0.2, 0.5))

# ---
# Step 4
# Ordered logit model
# ---

# Estimate ordered logit model with a unit variance
# that is just a matter of reparametrization i.e.,
# do not affect signs and significance of the coefficients
# and dot not affect at all the marginal effects
logit <- msel(z ~ w1 + w2, data = data, marginal = list("logistic" = 0))
summary(logit)

# Compare ordered probit and ordered logit models
# using Akaike and Bayesian information criteria
  # AIC
c(probit = AIC(model), logit = AIC(logit))
  # BIC
c(probit = BIC(model), logit = BIC(logit))

# Estimate some probabilities and marginal effects
  # probability P(z = 1 | w)
predict(logit, group = 1, type = "prob", newdata = newdata)
  # marginal effect of w2 on P(z = 1 | w)
predict(logit, group = 1, type = "prob", newdata = newdata, me = "w2")

# ---
# Step 5
# Semiparametric ordered choice model with 
# Gallant and Nychka distribution
# ---

# Estimate semiparametric model
pgn <- msel(z ~ w1 + w2, data = data, marginal = list("PGN" = 2))
summary(pgn)

# Estimate some probabilities and marginal effects
  # probability P(z = 3 | w)
predict(pgn, group = 3, type = "prob", newdata = newdata)
  # marginal effect of w2 on P(z = 3 | w)
predict(pgn, group = 3, type = "prob", newdata = newdata, me = "w2")

# Test the normality assumption via the likelihood ratio test
summary(lrtest_msel(model, pgn))

# Test the normality assumption via the Wald test
test_fn <- function(object)
{
  marginal_par <- coef(object, type = "marginal", eq = 1)
  return(marginal_par)
}
test_result <- test_msel(object = pgn, test = "wald", fn = test_fn)
summary(test_result)

 

# ---------------------------------------
# Simulated data example 2
# Heteroscedastic ordered probit model
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)

# Random errors
u <- rnorm(n, mean = 0, sd = 1)

# Coefficients of the mean equation
gamma <- c(-1, 2)

# Coefficients of the variance equation
gamma_het <- c(0.5, -1)

# Linear predictor (index) of the mean equation
li <- gamma[1] * w1 + gamma[2] * w2

# Linear predictor (index) of the variance equation
li_het <- gamma_het[1] * w2 + gamma_het[2] * w3

# Heteroscedastic stdandard deviation
# i.e., value of the variance equation
sd_het <- exp(li_het)

# Latent variable
z_star <- li + u * sd_het

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordinal outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, z = z)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(z ~ w1 + w2 | w2 + w3,
                   data = data)
summary(model)

# Compare the estimates and true values of the parameters
  # regression coefficients of the mean equation
gamma_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma, estimate = gamma_est)
  # regression coefficients of the variance equation
gamma_het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma_het, estimate = gamma_het_est)  
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   

# Likelihood-ratio test for the homoscedasticity
model0 <- msel(z ~ w1 + w2, data = data)
summary(lrtest_msel(model, model0))

# Wald test for the homoscedasticity
test_fn <- function(object)
{
  val <- coef(object, type = "coef_var", eq = 1)
  return(val)
}
test_result <- test_msel(model, test = "wald", fn = test_fn)
summary(test_result)

# ---
# Step 3
# Estimation of the probabilities and marginal effects
# ---

# Predict probability of the dependent variable
# equals to 2 for every observation in a sample
# P(z = 2 | w)
prob <- predict(model, group = 2, type = "prob")
head(prob)

# Calculate mean marginal effect of w2 on P(z = 1 | w)
mean(predict(model, group = 1, type = "prob", me = "w2"))

# Estimate conditional probabilities, linear predictors (indexes) and 
# heteroscedastic standard deviations for manually 
# provided observations.
  # new data
newdata <- data.frame(z = c(1, 1), 
                      w1 = c(0.5, 0.2), 
                      w2 = c(-0.3, 0.8),
                      w3 = c(0.6, 0.1))
  # probability P(z = 2 | w)
predict(model, group = 2, type = "prob", newdata = newdata)
  # linear predictor (index)
predict(model, type = "li", newdata = newdata)
  # standard deviation
predict(model, type = "sd", newdata = newdata)
  # marginal effect of w3 on P(z = 3 | w)
predict(model, group = 3, type = "prob", newdata = newdata, me = "w3")
  # marginal effect of w2 on the standard error
predict(model, group = 2, type = "sd", newdata = newdata, me = "w2")
  # discrete marginal effect: 
  # P(Z = 2 | w1 = 0.5, w2) - P(Z = 2 | w1 = 0.2, w2)
predict(model, group = 2, type = "prob", newdata = newdata,
        me = "w2", eps = c(0.2, 0.5))



# ---------------------------------------
# Simulated data example 3
# Bivariate ordered probit model with 
# heteroscedastic second equation
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)
w4 <- runif(n = n, min = -1, max = 1)

# Covariance matrix of random errors
rho <- 0.5
sigma <- matrix(c(1,   rho,
                  rho, 1), 
                nrow = 2)

# Random errors
u <- mnorm::rmnorm(n = n, mean = c(0, 0), sigma = sigma)

# Coefficients
gamma1 <- c(-1, 2)
gamma2 <- c(1, 1.5)

# Coefficients of the variance equation
gamma2_het <- c(0.5, -1)

# Linear predictors (indexes)
li1 <- gamma1[1] * w1 + gamma1[2] * w2
li2 <- gamma2[1] * w2 + gamma2[2] * w3

# Linear predictor (index) of the variance equation
li2_het <- gamma2_het[1] * w2 + gamma2_het[2] * w4

# Heteroscedastic stdandard deviation
# i.e. value of variance equation
sd2_het <- exp(li2_het)

# Latent variables
z1_star <- li1 + u[, 1]
z2_star <- li2 + u[, 2] * sd2_het

# Cuts
cuts1 <- c(-1, 0.5, 2)
cuts2 <- c(-2, 0)

# Observable ordinal outcome
  # first outcome
z1                                               <- rep(0, n)
z1[(z1_star > cuts1[1]) & (z1_star <= cuts1[2])] <- 1
z1[(z1_star > cuts1[2]) & (z1_star <= cuts1[3])] <- 2
z1[z1_star > cuts1[3]]                           <- 3
  # second outcome
z2                                               <- rep(0, n)
z2[(z2_star > cuts2[1]) & (z2_star <= cuts2[2])] <- 1
z2[z2_star > cuts2[2]]                           <- 2
  # distribution
table(z1, z2)

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, 
                   w4 = w4, z1 = z1, z2 = z2)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(list(z1 ~ w1 + w2,
                   z2 ~ w2 + w3 | w2 + w4),
              data = data)
summary(model)

# Compare the estimates and true values of parameters
  # regression coefficients of the first equation
gamma1_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma1, estimate = gamma1_est)
  # regression coefficients of the second equation
gamma2_est <- coef(model, type = "coef", eq = 2)
cbind(true = gamma2, estimate = gamma2_est)
  # cuts of the first equation
cuts1_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts1, estimate = cuts1_est)   
  # cuts of the second equation
cuts2_est <- coef(model, type = "cuts", eq = 2)
cbind(true = cuts2, estimate = cuts2_est) 
  # correlation coefficients
rho_est <- coef(model, type = "cov1", eq = c(1, 2))
cbind(true = rho, estimate = rho_est)  
  # regression coefficients of the variance equation
gamma2_het_est <- coef(model, type = "coef_var", eq = 2)
cbind(true = gamma2_het, estimate = gamma2_het_est)

# ---
# Step 3
# Estimation of the probabilities and linear predictors (indexes)
# ---

# Predict probability P(z1 = 2, z2 = 0 | w)
prob <- predict(model, group = c(2, 0), type = "prob")
head(prob)

# Calculate mean marginal effect of w2 on:
  # P(z1 = 1 | w)
mean(predict(model, group = c(1, -1), type = "prob", me = "w2"))
  # P(z1 = 1, z2 = 0 | w)
mean(predict(model, group = c(1, 0), type = "prob", me = "w2"))

# Calculate conditional probabilities and linear predictors (indexes)
# for the manually provided observations.
  # new data
newdata <- data.frame(z1 = c(1, 1), 
                      z2 = c(1, 1),
                      w1 = c(0.5, 0.2), 
                      w2 = c(-0.3, 0.8),
                      w3 = c(0.6, 0.1),
                      w4 = c(0.3, -0.5))
  # probability P(z1 = 2, z2 = 0 | w)
predict(model, group = c(2, 0), type = "prob", newdata = newdata)
  # linear predictor (index)
predict(model, type = "li", newdata = newdata)  
  # marginal probability P(z2 = 1 | w)
predict(model, group = c(-1, 1), type = "prob", newdata = newdata) 
  # marginal probability P(z1 = 3 | w)
predict(model, group = c(3, -1), type = "prob", newdata = newdata)
  # conditional probability P(z1 = 2 | z2 = 0, w)
predict(model, group = c(2, 0), given_ind = 2,
        type = "prob", newdata = newdata) 
  # conditional probability P(z2 = 1 | z1 = 3, w)
predict(model, group = c(3, 1), given_ind = 1,
        type = "prob", newdata = newdata) 
  # marginal effect of w4 on P(Z2 = 2 | w)
predict(model, group = c(-1, 2),
        type = "prob", newdata = newdata, me = "w4")   
  # marginal effect of w4 on P(z1 = 3, Z2 = 2 | w)
predict(model, group = c(3, 2),
        type = "prob", newdata = newdata, me = "w4") 
  # marginal effect of w4 on P(z1 = 3 | z2 = 2, w)
predict(model, group = c(3, 2), given_ind = 2,
        type = "prob", newdata = newdata, me = "w4")         

# ---
# Step 4
# Replication under the non-random sample selection
# ---

# Suppose that z2 is unobservable when z1 = 1 or z1 = 3
z2[(z1 == 1) | (z1 == 3)] <- -1
data$z2 <- z2

# Replicate the estimation procedure
model <- msel(list(z1 ~ w1 + w2,
                   z2 ~ w2 + w3 | w2 + w4),
              cov_type = "gop", data = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the first equation
gamma1_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma1, estimate = gamma1_est)
  # regression coefficients of the second equation
gamma2_est <- coef(model, type = "coef", eq = 2)  
cbind(true = gamma2, estimate = gamma2_est)
  # cuts of the first equation
cuts1_est <- coef(model, type = "cuts", eq = 1)  
cbind(true = cuts1, estimate = cuts1_est)   
  # cuts of the second equation
cuts2_est <- coef(model, type = "cuts", eq = 2)  
cbind(true = cuts2, estimate = cuts2_est) 
  # correlation coefficient
rho_est <- coef(model, type = "cov1", eq = c(1, 2))
cbind(true = rho, estimate = rho_est)  
  # regression coefficients of the variance equation
gamma2_het_est <- coef(model, type = "coef_var", eq = 2)  
cbind(true = gamma2_het, estimate = gamma2_het_est)

# ---
# Step 5
# Semiparametric model with marginal logistic and PGN distributions
# ---

# Estimate the model
model <- msel(list(z1 ~ w1 + w2,
                   z2 ~ w2 + w3 | w2 + w4),
              data = data, marginal = list(PGN = 3, logistic = NULL))
summary(model)



# ---------------------------------------
# Simulated data example 4
# Heckman model with ordinal 
# selection mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)

# Random errors
rho    <- 0.5
var.y  <- 0.3
sd.y   <- sqrt(var.y)
sigma  <- matrix(c(1,          rho * sd.y,
                   rho * sd.y, var.y),
                 nrow = 2)
errors <- mnorm::rmnorm(n = n, mean = c(0, 0), sigma = sigma)
u      <- errors[, 1]
eps    <- errors[, 2]

# Coefficients
gamma <- c(-1, 2)
beta  <- c(1, -1, 1)

# Linear predictor (index)
li   <- gamma[1] * w1 + gamma[2] * w2
li.y <- beta[1] + beta[2] * w1 + beta[3] * w3

# Latent variable
z_star <- li + u
y_star <- li.y + eps

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordered outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Observable continuous outcome such
# that outcome 'y' is observable only 
# when 'z > 1' and unobservable otherwise
# i.e. when 'z <= 1' we code 'y' as 'NA'
y         <- y_star
y[z <= 1] <- NA

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, 
                   z = z, y = y)

# ---
# Step 2
# Estimation of parameters
# ---

# Estimation
model <- msel(z ~ w1 + w2, y ~ w1 + w3, data = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the ordinal equation
gamma_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma, estimate = gamma_est)
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   
  # regression coefficients of the continuous equation
beta_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
cbind(true = beta, estimate = beta_est)  
  # variance
var.y_est <- coef(model, type = "var", eq2 = 1, regime = 0)
cbind(true = var.y, estimate = var.y_est)
  # covariance
cov_est <- coef(model, type = "cov12", eq = 1, eq2 = 1)
cbind(true = rho * sd.y, estimate = cov_est)

# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z = 1, 
                      y = 1,
                      w1 = 0.1, 
                      w2 = 0.2,
                      w3 = 0.3)

# Predict the unconditional expectation of the continuous outcome
# E(y | w)
predict(model, group = -1, group2 = 0, newdata = newdata)

# Predict the conditional expectations of the continuous outcome
  # E(y | z = 2, w)
predict(model, group = 2, group2 = 0, newdata = newdata)
  # E(y | z = 0, w)
predict(model, group = 0, group2 = 0, newdata = newdata)

# ---
# Step 4
# Classic Heckman's two-step estimation procedure
# ---

# Estimate the model by using the two-step estimator
model_ts <- msel(z ~ w1 + w2, y ~ w1 + w3,
                 data = data, estimator = "2step")
summary(model_ts)

# Check the estimates accuracy
tbl <- cbind(true    = beta, 
             ml      = model$coef2[[1]][1, ],
             twostep = model_ts$coef2[[1]][1, -4])  
print(tbl)

# ---
# Step 5
# Semiparametric estimation procedure
# ---

# Estimate the model using Lee's method 
# assuming logistic distribution of the
# random errors of the selection equation
model_Lee <- msel(z ~ w1 + w2, 
                  y ~ w1 + w3,
                  data = data, estimator = "2step",
                  marginal = list(logistic = NULL))
summary(model_Lee)

# One step estimation is also available as well
# as more complex marginal distributions.
# Consider random errors in selection equation
# following PGN distribution with three parameters.
model_sp <- msel(z ~ w1 + w2, 
                 y ~ w1 + w3,
                 data = data, marginal = list(PGN = 3))
summary(model_sp)

# To additionally relax normality assumption of
# random error of continuous equation it is possible
# to use Newey's two-step procedure.
model_Newey <- msel(z ~ w1 + w2, 
                    y ~ w1 + w3,
                    data      = data,    marginal = list(logistic = 0),
                    estimator = "2step", degrees  = 2)
summary(model_Newey)



# ---------------------------------------
# Simulated data example 5
# Endogenous switching model with 
# heteroscedastic ordered selection 
# mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)

# Random errors
rho_0    <- -0.8
rho_1    <- -0.7
var2_0   <- 0.9
var2_1   <- 1
sd_y_0   <- sqrt(var2_0)
sd_y_1   <- sqrt(var2_1)
cor_y_01 <- 0.7
cov2_01  <- sd_y_0 * sd_y_1 * cor_y_01
cov2_z_0 <- rho_0 * sd_y_0
cov2_z_1 <- rho_1 * sd_y_1
sigma    <- matrix(c(1,        cov2_z_0, cov2_z_1,
                     cov2_z_0, var2_0,   cov2_01,
                     cov2_z_1, cov2_01,  var2_1),
                   nrow = 3)
errors   <- mnorm::rmnorm(n = n, mean = c(0, 0, 0), sigma = sigma)
u        <- errors[, 1]
eps_0    <- errors[, 2]
eps_1    <- errors[, 3]

# Coefficients
gamma     <- c(-1, 2)
gamma_het <- c(0.5, -1)
beta_0    <- c(1, -1, 1)
beta_1    <- c(2, -1.5, 0.5)

# Linear predictor (index) of the ordinal equation
  # mean
li <- gamma[1] * w1 + gamma[2] * w2
  # variance
li_het <- gamma_het[1] * w2 + gamma_het[2] * w3

# Linear predictor (index) of the continuous equation
  # regime 0
li_y_0 <- beta_0[1] + beta_0[2] * w1 + beta_0[3] * w3
  # regime 1
li_y_1 <- beta_1[1] + beta_1[2] * w1 + beta_1[3] * w3

# Latent variable
z_star   <- li + u * exp(li_het)
y_0_star <- li_y_0 + eps_0
y_1_star <- li_y_1 + eps_1

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordinal outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Observable continuous outcome such that y' is 
# observable in regime 1 when 'z = 1', 
# observable in regime 0 when 'z <= 1',
# unobservable           when 'z = 0'
y         <- rep(NA, n)
y[z == 0] <- NA
y[z == 1] <- y_0_star[z == 1]
y[z > 1]  <- y_1_star[z > 1]

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, 
                   z  = z,  y  = y)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign groups
groups <- matrix(0:3, ncol = 1)
groups2 <- matrix(c(-1, 0, 1, 1), ncol = 1)

# Estimation
model <- msel(list(z ~ w1 + w2 | w2 + w3),
                   list(y ~ w1 + w3),
                   groups = groups, groups2 = groups2,
                   data = data)
summary(model)

# Compare estimates and true values of parameters
  # regression coefficients of the ordinal equation
gamma_est     <- coef(model, type = "coef",     eq = 1)
gamma_het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma,     estimate = gamma_est)
cbind(true = gamma_het, estimate = gamma_het_est)
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   
  # regression coefficients of the continuous equation
beta_0_test <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta_1_test <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta_0, estimate = beta_0_test) 
cbind(true = beta_1, estimate = beta_1_test)
  # variances
var2_0_est <- coef(model, type = "var", eq2 = 1, regime = 0)
var2_1_est <- coef(model, type = "var", eq2 = 1, regime = 1)
cbind(true = c(var2_0, var2_1), estimate = c(var2_0_est, var2_1_est)) 
  # covariances between the random errors
cov2_z_0_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 0)
cov2_z_1_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 1)
cbind(true     = c(cov2_z_0,     cov2_z_1), 
      estimate = c(cov2_z_0_est, cov2_z_1_est))

# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z  = 1,   y  = 1,
                      w1 = 0.1, w2 = 0.2, w3 = 0.3)

# Predict the unconditional expectation of the 
# continuous outcome E(yr | w)
  # regime 0 
predict(model, group = -1, group2 = 0, newdata = newdata)
  # regime 1
predict(model, group = -1, group2 = 1, newdata = newdata)

# Predict the conditional expectations of the continuous outcome
# given condition 'z == 0' for regime 1 i.e., E(y1 | z = 0, w)
predict(model, group = 0, group2 = 1, newdata = newdata)



# ---------------------------------------
# Simulated data example 6
# Endogenous switching model with 
# multivariate heteroscedastic ordered 
# selection mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)
w4 <- runif(n = n, min = -1, max = 1)

# Random errors
rho_z1_z2 <- 0.5
rho_y0_z1 <- 0.6
rho_y0_z2 <- 0.7
rho_y1_z1 <- 0.65
rho_y1_z2 <- 0.75
var20     <- 0.9
var21     <- 1
sd_y0     <- sqrt(var20)
sd_y1     <- sqrt(var21)
cor_y01   <- 0.7
cov201    <- sd_y0 * sd_y1 * cor_y01
cov20_z1  <- rho_y0_z1 * sd_y0
cov21_z1  <- rho_y1_z1 * sd_y1
cov20_z2  <- rho_y0_z2 * sd_y0
cov21_z2  <- rho_y1_z2 * sd_y1
sigma     <- matrix(c(1,         rho_z1_z2,  cov20_z1,  cov21_z1, 
                      rho_z1_z2, 1,          cov20_z2,  cov21_z2,
                      cov20_z1,  cov20_z2,   var20,     cov201,
                      cov21_z1,  cov21_z2,   cov201,    var21),
                    nrow = 4)
errors    <- mnorm::rmnorm(n = n, mean = c(0, 0, 0, 0), sigma = sigma)
u1        <- errors[, 1]
u2        <- errors[, 2]
eps0      <- errors[, 3]
eps1      <- errors[, 4]

# Coefficients
gamma1     <- c(-1, 2)
gamma1_het <- c(0.5, -1)
gamma2     <- c(1, 1)
beta0      <- c(1, -1, 1, -1.2)
beta1      <- c(2, -1.5, 0.5, 1.2)

# Linear index (predictor) of the ordinal equation
  # mean
li1 <- gamma1[1] * w1 + gamma1[2] * w2
li2 <- gamma2[1] * w1 + gamma2[2] * w3
  # variance
li1_het <- gamma1_het[1] * w2 + gamma1_het[2] * w3

# Linear predictor (index) of the continuous equation
  # regime 0
li_y0 <- beta0[1] + beta0[2] * w1 + beta0[3] * w3 + beta0[4] * w4
  # regime 1
li_y1 <- beta1[1] + beta1[2] * w1 + beta1[3] * w3 + beta1[4] * w4

# Latent variables
z1_star <- li1 + u1 * exp(li1_het)
z2_star <- li2 + u2
y0_star <- li_y0 + eps0
y1_star <- li_y1 + eps1

# Cuts
cuts1 <- c(-1, 1)
cuts2 <- c(0)

# Observable ordered outcome
  # first
z1                                               <- rep(0, n)
z1[(z1_star > cuts1[1]) & (z1_star <= cuts1[2])] <- 1
z1[z1_star > cuts1[2]]                           <- 2
  # second
z2                     <- rep(0, n)
z2[z2_star > cuts2[1]] <- 1
table(z1, z2)

# Observable continuous outcome such
# that outcome 'y' is 
# in regime 0  when 'z1 == 1', 
# in regime 1  when 'z1 == 0' or 'z1 == 2',
# unobservable when 'z2 == 0'
y          <- rep(NA, n)
y[z1 == 1] <- y0_star[z1 == 1]
y[z1 != 1] <- y1_star[z1 != 1]
y[z2 == 0] <- NA

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, w4 = w4,
                   z1 = z1, z2 = z2, y  = y)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign groups
groups <- matrix(c(0, 0,
                   0, 1,
                   1, 0,
                   1, 1,
                   2, 0,
                   2, 1), 
                 byrow = TRUE, ncol = 2)
groups2 <- matrix(c(-1, 1, -1, 0, -1, 1), ncol = 1)

# Estimation
model <- msel(list(z1 ~ w1 + w2 | w2 + w3,
                   z2 ~ w1 + w3),
              y ~ w1 + w3 + w4,
              groups = groups, groups2 = groups2,
              data   = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the first ordered equation
gamma1_est      <- coef(model, type = "coef",     eq = 1)
gamma1__het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma1,     estimate = gamma1_est)
cbind(true = gamma1_het, estimate = gamma1__het_est)
  # regression coefficients of the second ordered equation
gamma2_est <- coef(model, type = "coef", eq = 2)
cbind(true = gamma2, estimate = gamma2_est)
  # cuts
cuts1_est <- coef(model, type = "cuts", eq = 1)
cuts2_est <- coef(model, type = "cuts", eq = 2)
cbind(true = cuts1, estimate = cuts1_est)   
cbind(true = cuts2, estimate = cuts2_est)  
  # regression coefficients of the continuous equation
beta0_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta1_est <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta0, estimate = beta0_est) 
cbind(true = beta1, estimate = beta1_est)
  # variances
var20_est <- coef(model, type = "var", eq2 = 1, regime = 0)
var21_est <- coef(model, type = "var", eq2 = 1, regime = 1)
cbind(true = c(var20, var21), estimate = c(var20_est, var21_est)) 
  # covariances
cov_y0_z1_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 0)
cov_y0_z2_est <- coef(model, type = "cov12", eq = 2, eq2 = 1, regime = 0)
cov_y1_z1_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 1)
cov_y1_z2_est <- coef(model, type = "cov12", eq = 2, eq2 = 1, regime = 1)
cbind(true     = c(cov20_z1, cov20_z2), 
      estimate = c(cov_y0_z1_est, cov_y0_z2_est))
cbind(true     = c(cov21_z1, cov21_z2), 
      estimate = c(cov_y1_z1_est,  cov_y1_z2_est))

# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z1 = 1,   z2 = 1,   y  = 1,
                      w1 = 0.1, w2 = 0.2, w3 = 0.3, w4 = 0.4)

# Predict the unconditional expectation of the continuous outcome
  # regime 0 
predict(model, group = c(-1, -1), group2 = 0, newdata = newdata)
  # regime 1
predict(model, group = c(-1, -1), group2 = 1, newdata = newdata)

# Predict the conditional expectations of the continuous outcome
# E(y1 | z1 = 2, z2 = 1, w)
predict(model, group = c(2, 1), group2 = 1, newdata = newdata)

# Marginal effect of w3 on E(y1 | z1 = 2, z2 = 1, w)
predict(model, group = c(2, 1), group2 = 1, newdata = newdata, me = "w3")

# ---
# Step 4
# Two-step estimation procedure
# ---

# For a comparison reasons let's estimate the model
# via the least squares
model.ls.0 <- lm(y ~ w1 + w3 + w4,
                 data = data[!is.na(data$y) & (data$z1 == 1), ])
model.ls.1 <- lm(y ~ w1 + w3 + w4,
                 data = data[!is.na(data$y) & (data$z1 != 1), ])


# Apply the two-step procedure
model_ts <-  msel(list(z1 ~ w1 + w2 | w2 + w3,
                       z2 ~ w1 + w3),
                  y ~ w1 + w3 + w4,
                  groups    = groups,  groups2 = groups2,
                  estimator = "2step", data    = data)
summary(model_ts)

# Use the two-step procedure with logistic marginal distributions
# that is multivariate generalization of the Lee's method
model_Lee <-  msel(list(z1 ~ w1 + w2 | w2 + w3,
                        z2 ~ w1 + w3),
                   y ~ w1 + w3 + w4,
                   marginal = list(logistic = NULL, logistic = NULL),
                   groups    = groups,  groups2 = groups2,
                   estimator = "2step", data    = data)
                        
# Apply the Newey's method
model_Newey <-  msel(list(z1 ~ w1 + w2 | w2 + w3,
                          z2 ~ w1 + w3),
                     y ~ w1 + w3 + w4,
                     marginal = list(logistic = NULL, logistic = NULL),
                     degrees = c(2, 3), groups = groups, groups2 = groups2,
                     estimator = "2step", data = data)

# Compare accuracy of the methods
  # beta0
tbl0 <- cbind(true    = beta0, 
              ls      = coef(model.ls.0),
              ml      = model$coef2[[1]][1, 1:length(beta0)],
              twostep = model_ts$coef2[[1]][1, 1:length(beta0)],
              Lee     = model_Lee$coef2[[1]][1, 1:length(beta0)],
              Newey   = model_Newey$coef2[[1]][1, 1:length(beta0)])  
print(tbl0)
  # beta1
tbl1 <- cbind(true    = beta1, 
              ls      = coef(model.ls.1),
              ml      = model$coef2[[1]][2, 1:length(beta1)],
              twostep = model_ts$coef2[[1]][2, 1:length(beta1)],
              Lee     = model_Lee$coef2[[1]][2, 1:length(beta1)],
              Newey   = model_Newey$coef2[[1]][2, 1:length(beta1)])  
print(tbl1)



# ---------------------------------------
# Simulated data example 7
# Endogenous multivariate switching model 
# with multivariate heteroscedastic
# ordered selection mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)
w4 <- runif(n = n, min = -1, max = 1)
w5 <- runif(n = n, min = -1, max = 1)

# Random errors
var_y0 <- 0.9
var_y1 <- 1
var_g0 <- 1.1
var_g1 <- 1.2
var_g2 <- 1.3
A      <- rWishart(1, 7, diag(7))[, , 1]
B      <- diag(sqrt(c(1, 1, var_y0, var_y1, 
                      var_g0, var_g1, var_g2)))
sigma  <-  B  %*% cov2cor(A) %*% B
errors <- mnorm::rmnorm(n = n, mean = rep(0, nrow(sigma)), sigma = sigma)
u1     <- errors[, 1]
u2     <- errors[, 2]
eps0_y <- errors[, 3]
eps1_y <- errors[, 4]
eps0_g <- errors[, 5]
eps1_g <- errors[, 6]
eps2_g <- errors[, 7]

# Coefficients
gamma1     <- c(-1, 2)
gamma1_het <- c(0.5, -1)
gamma2     <- c(1, 1)
beta0_y    <- c(1, -1, 1, -1.2)
beta1_y    <- c(2, -1.5, 0.5, 1.2)
beta0_g    <- c(-1, 1, 1, 1)
beta1_g    <- c(1, -1, 1, 1)
beta2_g    <- c(1, 1, -1, 1)

# Linear predictor (index) of the ordinal equation
  # mean
li1 <- gamma1[1] * w1 + gamma1[2] * w2
li2 <- gamma2[1] * w1 + gamma2[2] * w3
  # variance
li1_het <- gamma1_het[1] * w2 + gamma1_het[2] * w3

# Linear predictor (index) of the first continuous equation
  # regime 0
li_y0 <- beta0_y[1] + beta0_y[2] * w1 + beta0_y[3] * w3 + beta0_y[4] * w4
  # regime 1
li_y1 <- beta1_y[1] + beta1_y[2] * w1 + beta1_y[3] * w3 + beta1_y[4] * w4

# Linear predictor (index) of the second continuous equation
  # regime 0
li_g0 <- beta0_g[1] + beta0_g[2] * w2 + beta0_g[3] * w3 + beta0_g[4] * w5
  # regime 1
li_g1 <- beta1_g[1] + beta1_g[2] * w2 + beta1_g[3] * w3 + beta1_g[4] * w5
  # regime 2
li_g2 <- beta2_g[1] + beta2_g[2] * w2 + beta2_g[3] * w3 + beta2_g[4] * w5

# Latent variables
z1_star <- li1 + u1 * exp(li1_het)
z2_star <- li2 + u2
y0_star <- li_y0 + eps0_y
y1_star <- li_y1 + eps1_y
g0_star <- li_g0 + eps0_g
g1_star <- li_g1 + eps1_g
g2_star <- li_g2 + eps2_g

# Cuts
cuts1 <- c(-1, 1)
cuts2 <- c(0)

# Observable ordered outcome
  # first
z1                                               <- rep(0, n)
z1[(z1_star > cuts1[1]) & (z1_star <= cuts1[2])] <- 1
z1[z1_star > cuts1[2]]                           <- 2
  # second
z2                     <- rep(0, n)
z2[z2_star > cuts2[1]] <- 1
table(z1, z2)

# Observable continuous outcome such that outcome 'y' is 
# in regime 0  when 'z1 == 1', 
# in regime 1  when 'z1 == 0' or 'z1 == 2',
# unobservable when 'z2 == 0'
y          <- rep(NA, n)
y[z1 == 1] <- y0_star[z1 == 1]
y[z1 != 1] <- y1_star[z1 != 1]
y[z2 == 0] <- NA

#' # Observable continuous outcome such
# that outcome 'g' is 
# in regime 0 when 'z1 == z2', 
# in regime 1 when 'z1 > z2',
# in regime 2 when 'z1 < z2',
g           <- rep(NA, n)
g[z1 == z2] <- g0_star[z1 == z2]
g[z1 > z2]  <- g1_star[z1 > z2]
g[z1 < z2]  <- g2_star[z1 < z2]

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, w4 = w4, w5 = w5,
                   z1 = z1, z2 = z2, y  = y,  g  = g)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign groups
groups <- matrix(c(0, 0,
                   0, 1,
                   1, 0,
                   1, 1,
                   2, 0,
                   2, 1), 
                 byrow = TRUE, ncol = 2)

# Assign groups 2
  # prepare the matrix
groups2 <- matrix(NA, nrow = nrow(groups), ncol = 2)
  # fill the matrix
groups2[groups[, 1] == 1, 1]                        <- 0
groups2[(groups[, 1] == 0) | (groups[, 1] == 2), 1] <- 1
groups2[groups[, 2] == 0, 1]                        <- -1
groups2[groups[, 1] == groups[, 2], 2]              <- 0
groups2[groups[, 1] > groups[, 2], 2]               <- 1
groups2[groups[, 1] < groups[, 2], 2]               <- 2

# The structure of the model
cbind(groups, groups2)

# Estimation
model <- msel(list(z1 ~ w1 + w2 | w2 + w3, z2 ~ w1 + w3),
              list(y ~ w1 + w3 + w4, g ~ w2 + w3 + w5),
              groups = groups, groups2 = groups2, data = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the first ordered equation
gamma1_est     <- coef(model, type = "coef", eq = 1)
gamma1_het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma1,     estimate = gamma1_est)
cbind(true = gamma1_het, estimate = gamma1_het_est)
  # regression coefficients of the second ordered equation
gamma2_est <- coef(model, type = "coef", eq = 2)
cbind(true = gamma2, estimate = gamma2_est)
  # cuts
cuts1_est <- coef(model, type = "cuts", eq = 1)
cuts2_est <- coef(model, type = "cuts", eq = 2)
cbind(true = cuts1, estimate = cuts1_est)   
cbind(true = cuts2, estimate = cuts2_est)  
  # regression coefficients of the first continuous equation
beta0_y_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta1_y_est <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta0_y, estimate = beta0_y_est) 
cbind(true = beta1_y, estimate = beta1_y_est)
  # regression coefficients of the second continuous equation
beta0_g_est <- coef(model, type = "coef2", eq2 = 2, regime = 0)
beta1_g_est <- coef(model, type = "coef2", eq2 = 2, regime = 1)
beta2_g_est <- coef(model, type = "coef2", eq2 = 2, regime = 2)
cbind(true = beta0_g, estimate = beta0_g_est) 
cbind(true = beta1_g, estimate = beta1_g_est)
cbind(true = beta2_g, estimate = beta2_g_est)
  # variances of the first continuous equation
var_y0_est <- coef(model, type = "var", eq2 = 1, regime = 0)
var_y1_est <- coef(model, type = "var", eq2 = 1, regime = 1)
cbind(true = c(var_y0, var_y1), estimate = c(var_y0_est, var_y1_est)) 
  # variances of the second continuous equation
var_g0_est <- coef(model, type = "var", eq2 = 2, regime = 0)
var_g1_est <- coef(model, type = "var", eq2 = 2, regime = 1)
var_g2_est <- coef(model, type = "var", eq2 = 2, regime = 2)
cbind(true     = c(var_g0, var_g1, var_g2), 
      estimate = c(var_g0_est, var_g1_est, var_g2_est)) 
  # correlation between the ordinal equations
sigma12_est <- coef(model, type = "cov1", eq = c(1, 2))
cbind(true = c(sigma[1, 2]), estimate = sigma12_est)
  # covariances between the continuous and ordinal equations
cbind(true = sigma[1:2, 3], estimate = model$cov2[[1]][1, ])
cbind(true = sigma[1:2, 4], estimate = model$cov2[[1]][2, ])
cbind(true = sigma[1:2, 5], estimate = model$cov2[[2]][1, ])
cbind(true = sigma[1:2, 6], estimate = model$cov2[[2]][2, ])
cbind(true = sigma[1:2, 7], estimate = model$cov2[[2]][3, ])
  # covariances between the continuous equations
sigma2_est <- coef(model, type = "cov2")[[1]]
cbind(true     = c(sigma[4, 7], sigma[3, 5], sigma[4, 6]), 
      estimate = sigma2_est) 
      
# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z1 = 1,   z2 = 1,   y  = 1,   g  = 1,
                      w1 = 0.1, w2 = 0.2, w3 = 0.3, w4 = 0.4, w5 = 0.5)

# Predict unconditional expectation of the dependent variable
  # regime 0 for 'y' and regime 1 for 'g' i.e. E(y0 | w), E(g1 | w)
predict(model, group = c(-1, -1), group2 = c(0, 1), newdata = newdata)

# Predict conditional expectations of the dependent variable
# E(y0 | z1 = 2, z2 = 1, w), E(g1 | z1 = 2, z2 = 1, w)
predict(model, group = c(2, 1), group2 = c(0, 1), newdata = newdata)

# Marginal effect of w3 on 
# E(y1 | z1 = 2, z2 = 1, w) and E(g1 | z1 = 2, z2 = 1, w)
predict(model, group   = c(2, 1), group2 = c(0, 1), 
               newdata = newdata, me     = "w3")

# ---
# Step 4
# Two-step estimation procedure
# ---

# Provide manually selectivity terms
model2 <- msel(list(z1 ~ w1 + w2 | w2 + w3, z2 ~ w1 + w3),
               list(y ~ w1 + w3 + w4 + 
                        lambda1 + lambda2 + I(lambda1 * lambda2), 
                    g ~ w2 + w3 + w5 + lambda1 + lambda2),
               groups = groups, groups2   = groups2,
               data   = data,   estimator = "2step")
summary(model2)



# ---------------------------------------
# Simulated data example 8
# Multinomial endogenous switching and 
# selection model (probit)
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 10000

# Random errors
  # variances and correlations
sd.z2    <- sqrt(0.9)
cor.z    <- 0.3
sd.y0    <- sqrt(2)
cor.z1y0 <- 0.4
cor.z2y0 <- 0.7
sd.y1    <- sqrt(1.8)
cor.z1y1 <- 0.3
cor.z2y1 <- 0.6
cor.y    <- 0.8
  # the covariance matrix
sigma <- matrix(c(
1,                cor.z * sd.z2,            cor.z1y0 * sd.y0,         cor.z1y1 * sd.y1,
cor.z * sd.z2,    sd.z2 ^ 2,                cor.z2y0 * sd.z2 * sd.y0, cor.z2y1 * sd.z2 * sd.y1,
cor.z1y0 * sd.y0, cor.z2y0 * sd.z2 * sd.y0, sd.y0 ^ 2,                cor.y * sd.y0 * sd.y1,
cor.z1y1 * sd.y1, cor.z2y1 * sd.z2 * sd.y1, cor.y * sd.y0 * sd.y1,    sd.y1 ^ 2),
                ncol = 4, byrow = TRUE)
colnames(sigma) <- c("z1", "z2", "y0", "y1")
rownames(sigma) <- colnames(sigma)

# Simulate the random errors
errors <- rmnorm(n, c(0, 0, 0, 0), sigma)
u      <- errors[, 1:2]
eps    <- errors[, 3:4]

# Regressors (covariates)
x1 <- runif(n, -1, 1)
x2 <- runif(n, -1, 1)
x3 <- (x2 + runif(n, -1, 1)) / 2
W  <- cbind(1, x1, x2)
X  <- cbind(1, x1, x3)

# Coefficients
gamma0 <- c(0.1,    1,   1)
gamma1 <- c(0.2, -1.2, 0.8)
beta0  <- c(1,     -3,   4) 
beta1  <- c(1,      4,  -3)

# Linear predictors (indexes)
z1.li <- W %*% gamma0
z2.li <- W %*% gamma1
y0.li <- X %*% beta0
y1.li <- X %*% beta1

# Latent variables
z1.star <- z1.li + u[, 1]
z2.star <- z2.li + u[, 2]
y0.star <- y0.li + eps[, 1]
y1.star <- y1.li + eps[, 2]

# Obvservable variable as a dummy
z1 <- (z1.star > z2.star) & (z1.star > 0)
z2 <- (z2.star > z1.star) & (z2.star > 0)
z3 <- (z1 != 1) & (z2 != 1)

# Observable multinomial variable
z     <- rep(0, n)
z[z1] <- 0
z[z2] <- 1
z[z3] <- 2
table(z)

# Make unobservable values of the continuous outcome
y         <-  rep(NA, n)
y[z == 1] <- y0.star[z == 1]
y[z == 2] <- y1.star[z == 2]

# Data
data <- data.frame(z = z, y = y, x1 = x1, x2 = x2, x3 = x3)
            
# ---
# Step 2
# Estimation of the parameters
# ---

# Define the groups
groups3 <- c(0, 1, 2)
groups2 <- matrix(c(-1, 0, 1), ncol = 1)

# Two-step method
model <- msel(formula3  = z ~ x1 + x2, formula2 = y ~ x1 + x3, 
              groups3   = groups3,     groups2   = groups2,
              data      = data,        estimator = "2step",
              type3     = "probit")
summary(model)

# Compare estimates and true values of parameters
  # regression coefficients of the continuous equation
beta0_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta1_est <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta0, est = beta0_est[1:length(beta0)])
cbind(true = beta1, est = beta1_est[1:length(beta1)])
  # regression coefficients of the multinomial equations
gamma0_est <- coef(model, type = "coef3", eq3 = 0)
gamma1_est <- coef(model, type = "coef3", eq3 = 1)
cbind(true = gamma0, est = gamma0_est)
cbind(true = gamma1, est = gamma1_est)
  # compare the covariances between
  # z1 and z2
cbind(true = cor.z * sd.z2, 
      est  = coef(model, type = "cov3", eq3 = c(0, 1)))
  # z1 and y0
cbind(true = cor.z1y0 * sd.y0, 
      est  = beta0_est["lambda1_mn"]) 
  # z2 and y0
cbind(true = cor.z2y0 * sd.y0, 
      est  = beta0_est["lambda2_mn"])   
  # z1 and y1
cbind(true = cor.z1y1 * sd.y1, 
      est  = beta1_est["lambda1_mn"]) 
  # z2 and y1
cbind(true = cor.z2y1 * sd.y1, 
      est  = beta1_est["lambda2_mn"]) 
            
# ---
# Step 3
# Predictions and marginal effects
# ---  

# Unconditional expectation E(y1 | w) for every observation in a sample
predict(model, type = "val", group2 = 1, group3 = -1)

# Marginal effect of x1 on conditional expectation E(y0 | z = 1, w) 
# for every observation in a sample
predict(model, type = "val", group2 = 0, group3 = 1, me = "x1")    

# Calculate predictions and marginal effects
# for manually provided observations
# using aforementioned models.
newdata <- data.frame(z  = c(1, 1), 
                      y  = c(1, 1), 
                      x1 = c(0.5, 0.2), 
                      x2 = c(-0.3, 0.8),
                      x3 = c(0.6, -0.7))
                      
# Unconditional expectation E(y0 | w)
predict(model, type = "val", group2 = 0, group3 = -1, newdata = newdata)

# Conditional expectation E(y1 | z=2, w)
predict(model, type = "val", group2 = 1, group3 = 2, newdata = newdata)

# Marginal effect of x2 on E(y0 | z = 1, w)
predict(model, type = "val", group2  = 0, group3 = 1, 
               me   = "x2",  newdata = newdata)
  
# ---
# Step 4
# Multinomial logit selection
# ---                
        
# Two-step method
model2 <- msel(formula3  = z ~ x1 + x2, formula2 = y ~ x1 + x3, 
               groups3   = groups3,     groups2   = groups2,
               data      = data,        estimator = "2step",
               type3     = "logit")
summary(model2)    

# Compare the estimates
beta0_est2 <- coef(model2, type = "coef2", eq2 = 1, regime = 0)[]
beta1_est2 <- coef(model2, type = "coef2", eq2 = 1, regime = 1)

# beta0 coefficients
cbind(true = beta0, probit = beta0_est[1:3], logit = beta0_est2[1:3])
   
# beta1 coefficients
cbind(true = beta1, probit = beta1_est[1:3], logit = beta1_est2[1:3])            

             

# ---------------------------------------
# Simulated data example 9
# Multinomial endogenous switching and 
# selection model (logit)
# ---------------------------------------

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 10000

# Random errors
u      <- matrix(-log(-log(runif(n * 3))), nrow = n, ncol = 3)
tau0   <- matrix(c(0.6, -0.4, 0.3), ncol = 1)
tau1   <- matrix(c(-0.3, 0.5, 0.2), ncol = 1)
eps0   <- (u - 0.57721656649) %*% tau0 + rnorm(n)
eps1   <- (u - 0.57721656649) %*% tau1 + rnorm(n)

# Regressors (covariates)
x1 <- runif(n = n, min = -1, max = 1)
x2 <- runif(n = n, min = -1, max = 1)
x3 <- runif(n = n, min = -1, max = 1)

# Coefficients
gamma.0 <- c(0.2, -2,  2)
gamma.1 <- c(0.1,  2, -2)
beta.0  <- c(2,    2,  2)
beta.1  <- c(1,   -2,  2)

# Linear predictors (indexes)
z0.li <- gamma.0[1] + gamma.0[2] * x1 + gamma.0[3] * x2
z1.li <- gamma.1[1] + gamma.1[2] * x1 + gamma.1[3] * x2

# Latent variables
z0.star <- z0.li + u[, 1]
z1.star <- z1.li + u[, 2]
z2.star <- u[, 3]
y0.star <- beta.0[1] + beta.0[2] * x1 + beta.0[3] * x3 + eps0
y1.star <- beta.1[1] + beta.1[2] * x1 + beta.1[3] * x3 + eps1

# Observable multinomial variable
z                                            <- rep(2, n)
z[(z0.star > z1.star) & (z0.star > z2.star)] <- 0
z[(z1.star > z0.star) & (z1.star > z2.star)] <- 1
table(z)

# Unobservable values of the continuous outcome
y         <- rep(NA, n)
y[z == 0] <- y0.star[z == 0]
y[z == 1] <- y1.star[z == 1]

# Data
data <- data.frame(x1 = x1, x2 = x2, x3 = x3, z = z, y = y)
            
# ---
# Step 2
# Estimation of the parameters
# ---

# Define the groups
groups3 <- c(0, 1, 2)
groups2 <- c(0, 1, -1)

# Two-step estimator of Dubin-McFadden
model <- msel(formula3  = z ~ x1 + x2, formula2 = y ~ x1 + x3, 
              groups3   = groups3,     groups2   = groups2,
              data      = data,        estimator = "2step",
              type3     = "logit")
summary(model)

# Least squaes estimates (benchmark)
lm0 <- lm(y ~ x1 + x3, data = data[data$z == 0, ])
lm1 <- lm(y ~ x1 + x3, data = data[data$z == 1, ])


# Compare the estimates of beta0
cbind(true = beta.0,
      DMF  = coef(model, type = "coef2", eq2 = 1, regime = 0),
      LS   = coef(lm0))
      
# Compare the estimates of beta1
cbind(true = beta.1,
      DMF  = coef(model, type = "coef2", eq2 = 1, regime = 1),
      LS   = coef(lm1))

             
# ---------------------------------------
# CPS data example
# ---------------------------------------

# Set seed for reproducibility
set.seed(123)

# Upload data
data(cps)

# Prepare the variable for education
cps$educ                    <- NA
cps$educ[cps$basic == 1]    <- 0
cps$educ[cps$bachelor == 1] <- 1
cps$educ[cps$master == 1]   <- 2

# Labor supply (probit) model
f_work <- work ~ age + I(age ^ 2) + bachelor + master + health + 
                 slwage + nchild
model1 <- msel(f_work, data = cps)
summary(model1)
    
# Education choice (ordered probit) model
f_educ <- educ ~ age + I(age ^ 2) + sbachelor + smaster
model2 <- msel(f_educ, data = cps)
summary(model2)   

# Education choice (multinomial logit) model
model3 <- msel(formula3 = f_educ, data = cps, type3 = "logit")
summary(model3)  

# Education choice (multinomial probit) model
model4 <- msel(formula3 = f_educ, data = cps, type3 = "probit")
summary(model4) 

# Labor supply with endogenous ordinal education 
# treatment (recursive or hierarchical ordered probit) model
model5 <- msel(list(f_work, f_educ), data = cps)  
summary(model5)         

# Sample selection (on employment) Heckman's model
f_lwage                  <- lwage ~ age + I(age ^ 2) + 
                                    bachelor + master + health
model6                   <- msel(f_work, f_lwage, data = cps)
summary(model6)

# Ordinal endogenous education treatment with non-random 
# sample selection into employment
model7 <- msel(list(f_work, f_educ), f_lwage, data = cps)
summary(model7)
 
# Ordinal endogenous switching on education model with 
# non-random selection into employment
groups   <- cbind(c(1, 1, 1, 0, 0, 0),
                  c(0, 1, 2, 0, 1, 2))
groups2  <- matrix(c(0, 1, 2, -1, -1, -1), ncol = 1)
f_lwage2 <- lwage ~ age + I(age ^ 2) + health
model8   <- msel(list(f_work, f_educ), f_lwage2,
                      groups = groups, groups2 = groups2,
                      data   = cps)
summary(model8)

# Multinomial endogenous switching on education model with 
# non-random selection into employment
groups  <- matrix(c(1, 1, 1, 0, 0, 0), ncol = 1)
groups2 <- matrix(c(0, 1, 2, -1, -1, -1), ncol = 1)
groups3 <- c(0, 1, 2, 0, 1, 2)
model9  <- msel(f_work, f_lwage2, f_educ,
                groups    = groups,  groups2 = groups2, 
                groups3   = groups3, data    = cps,
                estimator = "2step")
summary(model9)


# ---------------------------------------
# Simulated data example 1
# Ordered probit and other univariate
# ordered choice models
# --------------------------------------

# ---
# Step 1
# Simulation of the data
# ---

# Load required package
library("mnorm")

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)

# Random errors
u <- rnorm(n = n, mean = 0, sd = 1)

# Coefficients
gamma <- c(-1, 2)

# Linear predictor (index)
li <- gamma[1] * w1 + gamma[2] * w2

# Latent variable
z_star <- li + u

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordinal outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Data
data <- data.frame(w1 = w1, w2 = w2, z = z)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(z ~ w1 + w2, data = data)
summary(model)

# Compare the estimates and true values of the parameters
  # regression coefficients
gamma_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma, estimate = gamma_est)
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   

# ---
# Step 3
# Estimation of the probabilities and marginal effects
# ---

# Predict conditional probability of the dependent variable
# equals to 2 for every observation in a sample.
# P(z = 2 | w)
prob <- predict(model, group = 2, type = "prob")
head(prob)

# Calculate mean marginal effect of w2 on P(z = 1 | w)
mean(predict(model, group = 1, type = "prob", me = "w2"))

# Calculate probabilities and marginal effects
# for manually provided observations.
  # new data
newdata <- data.frame(z = c(1, 1), 
                      w1 = c(0.5, 0.2), 
                      w2 = c(-0.3, 0.8))
  # probability P(z = 2 | w)
predict(model, group = 2, type = "prob", newdata = newdata)
  # linear predictor (index)
predict(model, type = "li", newdata = newdata)  
  # marginal effect of w1 on P(z = 2 | w)
predict(model, group = 2, type = "prob", newdata = newdata, me = "w1")
  # marginal effect of w1 and w2 on P(z = 3 | w)
predict(model, group   = 3,       type = "prob", 
               newdata = newdata, me   = c("w1", "w2"))
  # marginal effect of w2 on the linear predictor (index)
predict(model, group = 2, type = "li", newdata = newdata, me = "w2")
  # discrete marginal effect:
  # P(z = 2 | w1 = 0.5, w2) - P(z = 2 | w1 = 0.2, w2)
predict(model, group = 2,    type = "prob", newdata = newdata, 
               me    = "w2", eps  = c(0.2, 0.5))

# ---
# Step 4
# Ordered logit model
# ---

# Estimate ordered logit model with a unit variance
# that is just a matter of reparametrization i.e.,
# do not affect signs and significance of the coefficients
# and dot not affect at all the marginal effects
logit <- msel(z ~ w1 + w2, data = data, marginal = list("logistic" = 0))
summary(logit)

# Compare ordered probit and ordered logit models
# using Akaike and Bayesian information criteria
  # AIC
c(probit = AIC(model), logit = AIC(logit))
  # BIC
c(probit = BIC(model), logit = BIC(logit))

# Estimate some probabilities and marginal effects
  # probability P(z = 1 | w)
predict(logit, group = 1, type = "prob", newdata = newdata)
  # marginal effect of w2 on P(z = 1 | w)
predict(logit, group = 1, type = "prob", newdata = newdata, me = "w2")

# ---
# Step 5
# Semiparametric ordered choice model with 
# Gallant and Nychka distribution
# ---

# Estimate semiparametric model
pgn <- msel(z ~ w1 + w2, data = data, marginal = list("PGN" = 2))
summary(pgn)

# Estimate some probabilities and marginal effects
  # probability P(z = 3 | w)
predict(pgn, group = 3, type = "prob", newdata = newdata)
  # marginal effect of w2 on P(z = 3 | w)
predict(pgn, group = 3, type = "prob", newdata = newdata, me = "w2")

# Test the normality assumption via the likelihood ratio test
summary(lrtest_msel(model, pgn))

# Test the normality assumption via the Wald test
test_fn <- function(object)
{
  marginal_par <- coef(object, type = "marginal", eq = 1)
  return(marginal_par)
}
test_result <- test_msel(object = pgn, test = "wald", fn = test_fn)
summary(test_result)

 

# ---------------------------------------
# Simulated data example 2
# Heteroscedastic ordered probit model
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)

# Random errors
u <- rnorm(n, mean = 0, sd = 1)

# Coefficients of the mean equation
gamma <- c(-1, 2)

# Coefficients of the variance equation
gamma_het <- c(0.5, -1)

# Linear predictor (index) of the mean equation
li <- gamma[1] * w1 + gamma[2] * w2

# Linear predictor (index) of the variance equation
li_het <- gamma_het[1] * w2 + gamma_het[2] * w3

# Heteroscedastic stdandard deviation
# i.e., value of the variance equation
sd_het <- exp(li_het)

# Latent variable
z_star <- li + u * sd_het

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordinal outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, z = z)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(z ~ w1 + w2 | w2 + w3,
                   data = data)
summary(model)

# Compare the estimates and true values of the parameters
  # regression coefficients of the mean equation
gamma_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma, estimate = gamma_est)
  # regression coefficients of the variance equation
gamma_het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma_het, estimate = gamma_het_est)  
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   

# Likelihood-ratio test for the homoscedasticity
model0 <- msel(z ~ w1 + w2, data = data)
summary(lrtest_msel(model, model0))

# Wald test for the homoscedasticity
test_fn <- function(object)
{
  val <- coef(object, type = "coef_var", eq = 1)
  return(val)
}
test_result <- test_msel(model, test = "wald", fn = test_fn)
summary(test_result)

# ---
# Step 3
# Estimation of the probabilities and marginal effects
# ---

# Predict probability of the dependent variable
# equals to 2 for every observation in a sample
# P(z = 2 | w)
prob <- predict(model, group = 2, type = "prob")
head(prob)

# Calculate mean marginal effect of w2 on P(z = 1 | w)
mean(predict(model, group = 1, type = "prob", me = "w2"))

# Estimate conditional probabilities, linear predictors (indexes) and 
# heteroscedastic standard deviations for manually 
# provided observations.
  # new data
newdata <- data.frame(z = c(1, 1), 
                      w1 = c(0.5, 0.2), 
                      w2 = c(-0.3, 0.8),
                      w3 = c(0.6, 0.1))
  # probability P(z = 2 | w)
predict(model, group = 2, type = "prob", newdata = newdata)
  # linear predictor (index)
predict(model, type = "li", newdata = newdata)
  # standard deviation
predict(model, type = "sd", newdata = newdata)
  # marginal effect of w3 on P(z = 3 | w)
predict(model, group = 3, type = "prob", newdata = newdata, me = "w3")
  # marginal effect of w2 on the standard error
predict(model, group = 2, type = "sd", newdata = newdata, me = "w2")
  # discrete marginal effect: 
  # P(Z = 2 | w1 = 0.5, w2) - P(Z = 2 | w1 = 0.2, w2)
predict(model, group = 2, type = "prob", newdata = newdata,
        me = "w2", eps = c(0.2, 0.5))



# ---------------------------------------
# Simulated data example 3
# Bivariate ordered probit model with 
# heteroscedastic second equation
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)
w4 <- runif(n = n, min = -1, max = 1)

# Covariance matrix of random errors
rho <- 0.5
sigma <- matrix(c(1,   rho,
                  rho, 1), 
                nrow = 2)

# Random errors
u <- mnorm::rmnorm(n = n, mean = c(0, 0), sigma = sigma)

# Coefficients
gamma1 <- c(-1, 2)
gamma2 <- c(1, 1.5)

# Coefficients of the variance equation
gamma2_het <- c(0.5, -1)

# Linear predictors (indexes)
li1 <- gamma1[1] * w1 + gamma1[2] * w2
li2 <- gamma2[1] * w2 + gamma2[2] * w3

# Linear predictor (index) of the variance equation
li2_het <- gamma2_het[1] * w2 + gamma2_het[2] * w4

# Heteroscedastic stdandard deviation
# i.e. value of variance equation
sd2_het <- exp(li2_het)

# Latent variables
z1_star <- li1 + u[, 1]
z2_star <- li2 + u[, 2] * sd2_het

# Cuts
cuts1 <- c(-1, 0.5, 2)
cuts2 <- c(-2, 0)

# Observable ordinal outcome
  # first outcome
z1                                               <- rep(0, n)
z1[(z1_star > cuts1[1]) & (z1_star <= cuts1[2])] <- 1
z1[(z1_star > cuts1[2]) & (z1_star <= cuts1[3])] <- 2
z1[z1_star > cuts1[3]]                           <- 3
  # second outcome
z2                                               <- rep(0, n)
z2[(z2_star > cuts2[1]) & (z2_star <= cuts2[2])] <- 1
z2[z2_star > cuts2[2]]                           <- 2
  # distribution
table(z1, z2)

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, 
                   w4 = w4, z1 = z1, z2 = z2)

# ---
# Step 2
# Estimation of the parameters
# ---

# Estimation
model <- msel(list(z1 ~ w1 + w2,
                   z2 ~ w2 + w3 | w2 + w4),
              data = data)
summary(model)

# Compare the estimates and true values of parameters
  # regression coefficients of the first equation
gamma1_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma1, estimate = gamma1_est)
  # regression coefficients of the second equation
gamma2_est <- coef(model, type = "coef", eq = 2)
cbind(true = gamma2, estimate = gamma2_est)
  # cuts of the first equation
cuts1_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts1, estimate = cuts1_est)   
  # cuts of the second equation
cuts2_est <- coef(model, type = "cuts", eq = 2)
cbind(true = cuts2, estimate = cuts2_est) 
  # correlation coefficients
rho_est <- coef(model, type = "cov1", eq = c(1, 2))
cbind(true = rho, estimate = rho_est)  
  # regression coefficients of the variance equation
gamma2_het_est <- coef(model, type = "coef_var", eq = 2)
cbind(true = gamma2_het, estimate = gamma2_het_est)

# ---
# Step 3
# Estimation of the probabilities and linear predictors (indexes)
# ---

# Predict probability P(z1 = 2, z2 = 0 | w)
prob <- predict(model, group = c(2, 0), type = "prob")
head(prob)

# Calculate mean marginal effect of w2 on:
  # P(z1 = 1 | w)
mean(predict(model, group = c(1, -1), type = "prob", me = "w2"))
  # P(z1 = 1, z2 = 0 | w)
mean(predict(model, group = c(1, 0), type = "prob", me = "w2"))

# Calculate conditional probabilities and linear predictors (indexes)
# for the manually provided observations.
  # new data
newdata <- data.frame(z1 = c(1, 1), 
                      z2 = c(1, 1),
                      w1 = c(0.5, 0.2), 
                      w2 = c(-0.3, 0.8),
                      w3 = c(0.6, 0.1),
                      w4 = c(0.3, -0.5))
  # probability P(z1 = 2, z2 = 0 | w)
predict(model, group = c(2, 0), type = "prob", newdata = newdata)
  # linear predictor (index)
predict(model, type = "li", newdata = newdata)  
  # marginal probability P(z2 = 1 | w)
predict(model, group = c(-1, 1), type = "prob", newdata = newdata) 
  # marginal probability P(z1 = 3 | w)
predict(model, group = c(3, -1), type = "prob", newdata = newdata)
  # conditional probability P(z1 = 2 | z2 = 0, w)
predict(model, group = c(2, 0), given_ind = 2,
        type = "prob", newdata = newdata) 
  # conditional probability P(z2 = 1 | z1 = 3, w)
predict(model, group = c(3, 1), given_ind = 1,
        type = "prob", newdata = newdata) 
  # marginal effect of w4 on P(Z2 = 2 | w)
predict(model, group = c(-1, 2),
        type = "prob", newdata = newdata, me = "w4")   
  # marginal effect of w4 on P(z1 = 3, Z2 = 2 | w)
predict(model, group = c(3, 2),
        type = "prob", newdata = newdata, me = "w4") 
  # marginal effect of w4 on P(z1 = 3 | z2 = 2, w)
predict(model, group = c(3, 2), given_ind = 2,
        type = "prob", newdata = newdata, me = "w4")         

# ---
# Step 4
# Replication under the non-random sample selection
# ---

# Suppose that z2 is unobservable when z1 = 1 or z1 = 3
z2[(z1 == 1) | (z1 == 3)] <- -1
data$z2 <- z2

# Replicate the estimation procedure
model <- msel(list(z1 ~ w1 + w2,
                   z2 ~ w2 + w3 | w2 + w4),
              cov_type = "gop", data = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the first equation
gamma1_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma1, estimate = gamma1_est)
  # regression coefficients of the second equation
gamma2_est <- coef(model, type = "coef", eq = 2)  
cbind(true = gamma2, estimate = gamma2_est)
  # cuts of the first equation
cuts1_est <- coef(model, type = "cuts", eq = 1)  
cbind(true = cuts1, estimate = cuts1_est)   
  # cuts of the second equation
cuts2_est <- coef(model, type = "cuts", eq = 2)  
cbind(true = cuts2, estimate = cuts2_est) 
  # correlation coefficient
rho_est <- coef(model, type = "cov1", eq = c(1, 2))
cbind(true = rho, estimate = rho_est)  
  # regression coefficients of the variance equation
gamma2_het_est <- coef(model, type = "coef_var", eq = 2)  
cbind(true = gamma2_het, estimate = gamma2_het_est)

# ---
# Step 5
# Semiparametric model with marginal logistic and PGN distributions
# ---

# Estimate the model
model <- msel(list(z1 ~ w1 + w2,
                   z2 ~ w2 + w3 | w2 + w4),
              data = data, marginal = list(PGN = 3, logistic = NULL))
summary(model)



# ---------------------------------------
# Simulated data example 4
# Heckman model with ordinal 
# selection mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)

# Random errors
rho    <- 0.5
var.y  <- 0.3
sd.y   <- sqrt(var.y)
sigma  <- matrix(c(1,          rho * sd.y,
                   rho * sd.y, var.y),
                 nrow = 2)
errors <- mnorm::rmnorm(n = n, mean = c(0, 0), sigma = sigma)
u      <- errors[, 1]
eps    <- errors[, 2]

# Coefficients
gamma <- c(-1, 2)
beta  <- c(1, -1, 1)

# Linear predictor (index)
li   <- gamma[1] * w1 + gamma[2] * w2
li.y <- beta[1] + beta[2] * w1 + beta[3] * w3

# Latent variable
z_star <- li + u
y_star <- li.y + eps

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordered outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Observable continuous outcome such
# that outcome 'y' is observable only 
# when 'z > 1' and unobservable otherwise
# i.e. when 'z <= 1' we code 'y' as 'NA'
y         <- y_star
y[z <= 1] <- NA

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, 
                   z = z, y = y)

# ---
# Step 2
# Estimation of parameters
# ---

# Estimation
model <- msel(z ~ w1 + w2, y ~ w1 + w3, data = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the ordinal equation
gamma_est <- coef(model, type = "coef", eq = 1)
cbind(true = gamma, estimate = gamma_est)
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   
  # regression coefficients of the continuous equation
beta_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
cbind(true = beta, estimate = beta_est)  
  # variance
var.y_est <- coef(model, type = "var", eq2 = 1, regime = 0)
cbind(true = var.y, estimate = var.y_est)
  # covariance
cov_est <- coef(model, type = "cov12", eq = 1, eq2 = 1)
cbind(true = rho * sd.y, estimate = cov_est)

# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z = 1, 
                      y = 1,
                      w1 = 0.1, 
                      w2 = 0.2,
                      w3 = 0.3)

# Predict the unconditional expectation of the continuous outcome
# E(y | w)
predict(model, group = -1, group2 = 0, newdata = newdata)

# Predict the conditional expectations of the continuous outcome
  # E(y | z = 2, w)
predict(model, group = 2, group2 = 0, newdata = newdata)
  # E(y | z = 0, w)
predict(model, group = 0, group2 = 0, newdata = newdata)

# ---
# Step 4
# Classic Heckman's two-step estimation procedure
# ---

# Estimate the model by using the two-step estimator
model_ts <- msel(z ~ w1 + w2, y ~ w1 + w3,
                 data = data, estimator = "2step")
summary(model_ts)

# Check the estimates accuracy
tbl <- cbind(true    = beta, 
             ml      = model$coef2[[1]][1, ],
             twostep = model_ts$coef2[[1]][1, -4])  
print(tbl)

# ---
# Step 5
# Semiparametric estimation procedure
# ---

# Estimate the model using Lee's method 
# assuming logistic distribution of the
# random errors of the selection equation
model_Lee <- msel(z ~ w1 + w2, 
                  y ~ w1 + w3,
                  data = data, estimator = "2step",
                  marginal = list(logistic = NULL))
summary(model_Lee)

# One step estimation is also available as well
# as more complex marginal distributions.
# Consider random errors in selection equation
# following PGN distribution with three parameters.
model_sp <- msel(z ~ w1 + w2, 
                 y ~ w1 + w3,
                 data = data, marginal = list(PGN = 3))
summary(model_sp)

# To additionally relax normality assumption of
# random error of continuous equation it is possible
# to use Newey's two-step procedure.
model_Newey <- msel(z ~ w1 + w2, 
                    y ~ w1 + w3,
                    data      = data,    marginal = list(logistic = 0),
                    estimator = "2step", degrees  = 2)
summary(model_Newey)



# ---------------------------------------
# Simulated data example 5
# Endogenous switching model with 
# heteroscedastic ordered selection 
# mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)

# Random errors
rho_0    <- -0.8
rho_1    <- -0.7
var2_0   <- 0.9
var2_1   <- 1
sd_y_0   <- sqrt(var2_0)
sd_y_1   <- sqrt(var2_1)
cor_y_01 <- 0.7
cov2_01  <- sd_y_0 * sd_y_1 * cor_y_01
cov2_z_0 <- rho_0 * sd_y_0
cov2_z_1 <- rho_1 * sd_y_1
sigma    <- matrix(c(1,        cov2_z_0, cov2_z_1,
                     cov2_z_0, var2_0,   cov2_01,
                     cov2_z_1, cov2_01,  var2_1),
                   nrow = 3)
errors   <- mnorm::rmnorm(n = n, mean = c(0, 0, 0), sigma = sigma)
u        <- errors[, 1]
eps_0    <- errors[, 2]
eps_1    <- errors[, 3]

# Coefficients
gamma     <- c(-1, 2)
gamma_het <- c(0.5, -1)
beta_0    <- c(1, -1, 1)
beta_1    <- c(2, -1.5, 0.5)

# Linear predictor (index) of the ordinal equation
  # mean
li <- gamma[1] * w1 + gamma[2] * w2
  # variance
li_het <- gamma_het[1] * w2 + gamma_het[2] * w3

# Linear predictor (index) of the continuous equation
  # regime 0
li_y_0 <- beta_0[1] + beta_0[2] * w1 + beta_0[3] * w3
  # regime 1
li_y_1 <- beta_1[1] + beta_1[2] * w1 + beta_1[3] * w3

# Latent variable
z_star   <- li + u * exp(li_het)
y_0_star <- li_y_0 + eps_0
y_1_star <- li_y_1 + eps_1

# Cuts
cuts <- c(-1, 0.5, 2)

# Observable ordinal outcome
z                                           <- rep(0, n)
z[(z_star > cuts[1]) & (z_star <= cuts[2])] <- 1
z[(z_star > cuts[2]) & (z_star <= cuts[3])] <- 2
z[z_star > cuts[3]]                         <- 3
table(z)

# Observable continuous outcome such that y' is 
# observable in regime 1 when 'z = 1', 
# observable in regime 0 when 'z <= 1',
# unobservable           when 'z = 0'
y         <- rep(NA, n)
y[z == 0] <- NA
y[z == 1] <- y_0_star[z == 1]
y[z > 1]  <- y_1_star[z > 1]

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, 
                   z  = z,  y  = y)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign groups
groups <- matrix(0:3, ncol = 1)
groups2 <- matrix(c(-1, 0, 1, 1), ncol = 1)

# Estimation
model <- msel(list(z ~ w1 + w2 | w2 + w3),
                   list(y ~ w1 + w3),
                   groups = groups, groups2 = groups2,
                   data = data)
summary(model)

# Compare estimates and true values of parameters
  # regression coefficients of the ordinal equation
gamma_est     <- coef(model, type = "coef",     eq = 1)
gamma_het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma,     estimate = gamma_est)
cbind(true = gamma_het, estimate = gamma_het_est)
  # cuts
cuts_est <- coef(model, type = "cuts", eq = 1)
cbind(true = cuts, estimate = cuts_est)   
  # regression coefficients of the continuous equation
beta_0_test <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta_1_test <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta_0, estimate = beta_0_test) 
cbind(true = beta_1, estimate = beta_1_test)
  # variances
var2_0_est <- coef(model, type = "var", eq2 = 1, regime = 0)
var2_1_est <- coef(model, type = "var", eq2 = 1, regime = 1)
cbind(true = c(var2_0, var2_1), estimate = c(var2_0_est, var2_1_est)) 
  # covariances between the random errors
cov2_z_0_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 0)
cov2_z_1_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 1)
cbind(true     = c(cov2_z_0,     cov2_z_1), 
      estimate = c(cov2_z_0_est, cov2_z_1_est))

# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z  = 1,   y  = 1,
                      w1 = 0.1, w2 = 0.2, w3 = 0.3)

# Predict the unconditional expectation of the 
# continuous outcome E(yr | w)
  # regime 0 
predict(model, group = -1, group2 = 0, newdata = newdata)
  # regime 1
predict(model, group = -1, group2 = 1, newdata = newdata)

# Predict the conditional expectations of the continuous outcome
# given condition 'z == 0' for regime 1 i.e., E(y1 | z = 0, w)
predict(model, group = 0, group2 = 1, newdata = newdata)



# ---------------------------------------
# Simulated data example 6
# Endogenous switching model with 
# multivariate heteroscedastic ordered 
# selection mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)
w4 <- runif(n = n, min = -1, max = 1)

# Random errors
rho_z1_z2 <- 0.5
rho_y0_z1 <- 0.6
rho_y0_z2 <- 0.7
rho_y1_z1 <- 0.65
rho_y1_z2 <- 0.75
var20     <- 0.9
var21     <- 1
sd_y0     <- sqrt(var20)
sd_y1     <- sqrt(var21)
cor_y01   <- 0.7
cov201    <- sd_y0 * sd_y1 * cor_y01
cov20_z1  <- rho_y0_z1 * sd_y0
cov21_z1  <- rho_y1_z1 * sd_y1
cov20_z2  <- rho_y0_z2 * sd_y0
cov21_z2  <- rho_y1_z2 * sd_y1
sigma     <- matrix(c(1,         rho_z1_z2,  cov20_z1,  cov21_z1, 
                      rho_z1_z2, 1,          cov20_z2,  cov21_z2,
                      cov20_z1,  cov20_z2,   var20,     cov201,
                      cov21_z1,  cov21_z2,   cov201,    var21),
                    nrow = 4)
errors    <- mnorm::rmnorm(n = n, mean = c(0, 0, 0, 0), sigma = sigma)
u1        <- errors[, 1]
u2        <- errors[, 2]
eps0      <- errors[, 3]
eps1      <- errors[, 4]

# Coefficients
gamma1     <- c(-1, 2)
gamma1_het <- c(0.5, -1)
gamma2     <- c(1, 1)
beta0      <- c(1, -1, 1, -1.2)
beta1      <- c(2, -1.5, 0.5, 1.2)

# Linear index (predictor) of the ordinal equation
  # mean
li1 <- gamma1[1] * w1 + gamma1[2] * w2
li2 <- gamma2[1] * w1 + gamma2[2] * w3
  # variance
li1_het <- gamma1_het[1] * w2 + gamma1_het[2] * w3

# Linear predictor (index) of the continuous equation
  # regime 0
li_y0 <- beta0[1] + beta0[2] * w1 + beta0[3] * w3 + beta0[4] * w4
  # regime 1
li_y1 <- beta1[1] + beta1[2] * w1 + beta1[3] * w3 + beta1[4] * w4

# Latent variables
z1_star <- li1 + u1 * exp(li1_het)
z2_star <- li2 + u2
y0_star <- li_y0 + eps0
y1_star <- li_y1 + eps1

# Cuts
cuts1 <- c(-1, 1)
cuts2 <- c(0)

# Observable ordered outcome
  # first
z1                                               <- rep(0, n)
z1[(z1_star > cuts1[1]) & (z1_star <= cuts1[2])] <- 1
z1[z1_star > cuts1[2]]                           <- 2
  # second
z2                     <- rep(0, n)
z2[z2_star > cuts2[1]] <- 1
table(z1, z2)

# Observable continuous outcome such
# that outcome 'y' is 
# in regime 0  when 'z1 == 1', 
# in regime 1  when 'z1 == 0' or 'z1 == 2',
# unobservable when 'z2 == 0'
y          <- rep(NA, n)
y[z1 == 1] <- y0_star[z1 == 1]
y[z1 != 1] <- y1_star[z1 != 1]
y[z2 == 0] <- NA

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, w4 = w4,
                   z1 = z1, z2 = z2, y  = y)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign groups
groups <- matrix(c(0, 0,
                   0, 1,
                   1, 0,
                   1, 1,
                   2, 0,
                   2, 1), 
                 byrow = TRUE, ncol = 2)
groups2 <- matrix(c(-1, 1, -1, 0, -1, 1), ncol = 1)

# Estimation
model <- msel(list(z1 ~ w1 + w2 | w2 + w3,
                   z2 ~ w1 + w3),
              y ~ w1 + w3 + w4,
              groups = groups, groups2 = groups2,
              data   = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the first ordered equation
gamma1_est      <- coef(model, type = "coef",     eq = 1)
gamma1__het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma1,     estimate = gamma1_est)
cbind(true = gamma1_het, estimate = gamma1__het_est)
  # regression coefficients of the second ordered equation
gamma2_est <- coef(model, type = "coef", eq = 2)
cbind(true = gamma2, estimate = gamma2_est)
  # cuts
cuts1_est <- coef(model, type = "cuts", eq = 1)
cuts2_est <- coef(model, type = "cuts", eq = 2)
cbind(true = cuts1, estimate = cuts1_est)   
cbind(true = cuts2, estimate = cuts2_est)  
  # regression coefficients of the continuous equation
beta0_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta1_est <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta0, estimate = beta0_est) 
cbind(true = beta1, estimate = beta1_est)
  # variances
var20_est <- coef(model, type = "var", eq2 = 1, regime = 0)
var21_est <- coef(model, type = "var", eq2 = 1, regime = 1)
cbind(true = c(var20, var21), estimate = c(var20_est, var21_est)) 
  # covariances
cov_y0_z1_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 0)
cov_y0_z2_est <- coef(model, type = "cov12", eq = 2, eq2 = 1, regime = 0)
cov_y1_z1_est <- coef(model, type = "cov12", eq = 1, eq2 = 1, regime = 1)
cov_y1_z2_est <- coef(model, type = "cov12", eq = 2, eq2 = 1, regime = 1)
cbind(true     = c(cov20_z1, cov20_z2), 
      estimate = c(cov_y0_z1_est, cov_y0_z2_est))
cbind(true     = c(cov21_z1, cov21_z2), 
      estimate = c(cov_y1_z1_est,  cov_y1_z2_est))

# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z1 = 1,   z2 = 1,   y  = 1,
                      w1 = 0.1, w2 = 0.2, w3 = 0.3, w4 = 0.4)

# Predict the unconditional expectation of the continuous outcome
  # regime 0 
predict(model, group = c(-1, -1), group2 = 0, newdata = newdata)
  # regime 1
predict(model, group = c(-1, -1), group2 = 1, newdata = newdata)

# Predict the conditional expectations of the continuous outcome
# E(y1 | z1 = 2, z2 = 1, w)
predict(model, group = c(2, 1), group2 = 1, newdata = newdata)

# Marginal effect of w3 on E(y1 | z1 = 2, z2 = 1, w)
predict(model, group = c(2, 1), group2 = 1, newdata = newdata, me = "w3")

# ---
# Step 4
# Two-step estimation procedure
# ---

# For a comparison reasons let's estimate the model
# via the least squares
model.ls.0 <- lm(y ~ w1 + w3 + w4,
                 data = data[!is.na(data$y) & (data$z1 == 1), ])
model.ls.1 <- lm(y ~ w1 + w3 + w4,
                 data = data[!is.na(data$y) & (data$z1 != 1), ])


# Apply the two-step procedure
model_ts <-  msel(list(z1 ~ w1 + w2 | w2 + w3,
                       z2 ~ w1 + w3),
                  y ~ w1 + w3 + w4,
                  groups    = groups,  groups2 = groups2,
                  estimator = "2step", data    = data)
summary(model_ts)

# Use the two-step procedure with logistic marginal distributions
# that is multivariate generalization of the Lee's method
model_Lee <-  msel(list(z1 ~ w1 + w2 | w2 + w3,
                        z2 ~ w1 + w3),
                   y ~ w1 + w3 + w4,
                   marginal = list(logistic = NULL, logistic = NULL),
                   groups    = groups,  groups2 = groups2,
                   estimator = "2step", data    = data)
                        
# Apply the Newey's method
model_Newey <-  msel(list(z1 ~ w1 + w2 | w2 + w3,
                          z2 ~ w1 + w3),
                     y ~ w1 + w3 + w4,
                     marginal = list(logistic = NULL, logistic = NULL),
                     degrees = c(2, 3), groups = groups, groups2 = groups2,
                     estimator = "2step", data = data)

# Compare accuracy of the methods
  # beta0
tbl0 <- cbind(true    = beta0, 
              ls      = coef(model.ls.0),
              ml      = model$coef2[[1]][1, 1:length(beta0)],
              twostep = model_ts$coef2[[1]][1, 1:length(beta0)],
              Lee     = model_Lee$coef2[[1]][1, 1:length(beta0)],
              Newey   = model_Newey$coef2[[1]][1, 1:length(beta0)])  
print(tbl0)
  # beta1
tbl1 <- cbind(true    = beta1, 
              ls      = coef(model.ls.1),
              ml      = model$coef2[[1]][2, 1:length(beta1)],
              twostep = model_ts$coef2[[1]][2, 1:length(beta1)],
              Lee     = model_Lee$coef2[[1]][2, 1:length(beta1)],
              Newey   = model_Newey$coef2[[1]][2, 1:length(beta1)])  
print(tbl1)



# ---------------------------------------
# Simulated data example 7
# Endogenous multivariate switching model 
# with multivariate heteroscedastic
# ordered selection mechanism
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 1000

# Regressors (covariates)
w1 <- runif(n = n, min = -1, max = 1)
w2 <- runif(n = n, min = -1, max = 1)
w3 <- runif(n = n, min = -1, max = 1)
w4 <- runif(n = n, min = -1, max = 1)
w5 <- runif(n = n, min = -1, max = 1)

# Random errors
var_y0 <- 0.9
var_y1 <- 1
var_g0 <- 1.1
var_g1 <- 1.2
var_g2 <- 1.3
A      <- rWishart(1, 7, diag(7))[, , 1]
B      <- diag(sqrt(c(1, 1, var_y0, var_y1, 
                      var_g0, var_g1, var_g2)))
sigma  <-  B  %*% cov2cor(A) %*% B
errors <- mnorm::rmnorm(n = n, mean = rep(0, nrow(sigma)), sigma = sigma)
u1     <- errors[, 1]
u2     <- errors[, 2]
eps0_y <- errors[, 3]
eps1_y <- errors[, 4]
eps0_g <- errors[, 5]
eps1_g <- errors[, 6]
eps2_g <- errors[, 7]

# Coefficients
gamma1     <- c(-1, 2)
gamma1_het <- c(0.5, -1)
gamma2     <- c(1, 1)
beta0_y    <- c(1, -1, 1, -1.2)
beta1_y    <- c(2, -1.5, 0.5, 1.2)
beta0_g    <- c(-1, 1, 1, 1)
beta1_g    <- c(1, -1, 1, 1)
beta2_g    <- c(1, 1, -1, 1)

# Linear predictor (index) of the ordinal equation
  # mean
li1 <- gamma1[1] * w1 + gamma1[2] * w2
li2 <- gamma2[1] * w1 + gamma2[2] * w3
  # variance
li1_het <- gamma1_het[1] * w2 + gamma1_het[2] * w3

# Linear predictor (index) of the first continuous equation
  # regime 0
li_y0 <- beta0_y[1] + beta0_y[2] * w1 + beta0_y[3] * w3 + beta0_y[4] * w4
  # regime 1
li_y1 <- beta1_y[1] + beta1_y[2] * w1 + beta1_y[3] * w3 + beta1_y[4] * w4

# Linear predictor (index) of the second continuous equation
  # regime 0
li_g0 <- beta0_g[1] + beta0_g[2] * w2 + beta0_g[3] * w3 + beta0_g[4] * w5
  # regime 1
li_g1 <- beta1_g[1] + beta1_g[2] * w2 + beta1_g[3] * w3 + beta1_g[4] * w5
  # regime 2
li_g2 <- beta2_g[1] + beta2_g[2] * w2 + beta2_g[3] * w3 + beta2_g[4] * w5

# Latent variables
z1_star <- li1 + u1 * exp(li1_het)
z2_star <- li2 + u2
y0_star <- li_y0 + eps0_y
y1_star <- li_y1 + eps1_y
g0_star <- li_g0 + eps0_g
g1_star <- li_g1 + eps1_g
g2_star <- li_g2 + eps2_g

# Cuts
cuts1 <- c(-1, 1)
cuts2 <- c(0)

# Observable ordered outcome
  # first
z1                                               <- rep(0, n)
z1[(z1_star > cuts1[1]) & (z1_star <= cuts1[2])] <- 1
z1[z1_star > cuts1[2]]                           <- 2
  # second
z2                     <- rep(0, n)
z2[z2_star > cuts2[1]] <- 1
table(z1, z2)

# Observable continuous outcome such that outcome 'y' is 
# in regime 0  when 'z1 == 1', 
# in regime 1  when 'z1 == 0' or 'z1 == 2',
# unobservable when 'z2 == 0'
y          <- rep(NA, n)
y[z1 == 1] <- y0_star[z1 == 1]
y[z1 != 1] <- y1_star[z1 != 1]
y[z2 == 0] <- NA

#' # Observable continuous outcome such
# that outcome 'g' is 
# in regime 0 when 'z1 == z2', 
# in regime 1 when 'z1 > z2',
# in regime 2 when 'z1 < z2',
g           <- rep(NA, n)
g[z1 == z2] <- g0_star[z1 == z2]
g[z1 > z2]  <- g1_star[z1 > z2]
g[z1 < z2]  <- g2_star[z1 < z2]

# Data
data <- data.frame(w1 = w1, w2 = w2, w3 = w3, w4 = w4, w5 = w5,
                   z1 = z1, z2 = z2, y  = y,  g  = g)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign groups
groups <- matrix(c(0, 0,
                   0, 1,
                   1, 0,
                   1, 1,
                   2, 0,
                   2, 1), 
                 byrow = TRUE, ncol = 2)

# Assign groups 2
  # prepare the matrix
groups2 <- matrix(NA, nrow = nrow(groups), ncol = 2)
  # fill the matrix
groups2[groups[, 1] == 1, 1]                        <- 0
groups2[(groups[, 1] == 0) | (groups[, 1] == 2), 1] <- 1
groups2[groups[, 2] == 0, 1]                        <- -1
groups2[groups[, 1] == groups[, 2], 2]              <- 0
groups2[groups[, 1] > groups[, 2], 2]               <- 1
groups2[groups[, 1] < groups[, 2], 2]               <- 2

# The structure of the model
cbind(groups, groups2)

# Estimation
model <- msel(list(z1 ~ w1 + w2 | w2 + w3, z2 ~ w1 + w3),
              list(y ~ w1 + w3 + w4, g ~ w2 + w3 + w5),
              groups = groups, groups2 = groups2, data = data)
summary(model)

# Compare estimates and true values of the parameters
  # regression coefficients of the first ordered equation
gamma1_est     <- coef(model, type = "coef", eq = 1)
gamma1_het_est <- coef(model, type = "coef_var", eq = 1)
cbind(true = gamma1,     estimate = gamma1_est)
cbind(true = gamma1_het, estimate = gamma1_het_est)
  # regression coefficients of the second ordered equation
gamma2_est <- coef(model, type = "coef", eq = 2)
cbind(true = gamma2, estimate = gamma2_est)
  # cuts
cuts1_est <- coef(model, type = "cuts", eq = 1)
cuts2_est <- coef(model, type = "cuts", eq = 2)
cbind(true = cuts1, estimate = cuts1_est)   
cbind(true = cuts2, estimate = cuts2_est)  
  # regression coefficients of the first continuous equation
beta0_y_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta1_y_est <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta0_y, estimate = beta0_y_est) 
cbind(true = beta1_y, estimate = beta1_y_est)
  # regression coefficients of the second continuous equation
beta0_g_est <- coef(model, type = "coef2", eq2 = 2, regime = 0)
beta1_g_est <- coef(model, type = "coef2", eq2 = 2, regime = 1)
beta2_g_est <- coef(model, type = "coef2", eq2 = 2, regime = 2)
cbind(true = beta0_g, estimate = beta0_g_est) 
cbind(true = beta1_g, estimate = beta1_g_est)
cbind(true = beta2_g, estimate = beta2_g_est)
  # variances of the first continuous equation
var_y0_est <- coef(model, type = "var", eq2 = 1, regime = 0)
var_y1_est <- coef(model, type = "var", eq2 = 1, regime = 1)
cbind(true = c(var_y0, var_y1), estimate = c(var_y0_est, var_y1_est)) 
  # variances of the second continuous equation
var_g0_est <- coef(model, type = "var", eq2 = 2, regime = 0)
var_g1_est <- coef(model, type = "var", eq2 = 2, regime = 1)
var_g2_est <- coef(model, type = "var", eq2 = 2, regime = 2)
cbind(true     = c(var_g0, var_g1, var_g2), 
      estimate = c(var_g0_est, var_g1_est, var_g2_est)) 
  # correlation between the ordinal equations
sigma12_est <- coef(model, type = "cov1", eq = c(1, 2))
cbind(true = c(sigma[1, 2]), estimate = sigma12_est)
  # covariances between the continuous and ordinal equations
cbind(true = sigma[1:2, 3], estimate = model$cov2[[1]][1, ])
cbind(true = sigma[1:2, 4], estimate = model$cov2[[1]][2, ])
cbind(true = sigma[1:2, 5], estimate = model$cov2[[2]][1, ])
cbind(true = sigma[1:2, 6], estimate = model$cov2[[2]][2, ])
cbind(true = sigma[1:2, 7], estimate = model$cov2[[2]][3, ])
  # covariances between the continuous equations
sigma2_est <- coef(model, type = "cov2")[[1]]
cbind(true     = c(sigma[4, 7], sigma[3, 5], sigma[4, 6]), 
      estimate = sigma2_est) 
      
# ---
# Step 3
# Estimation of the expectations and marginal effects
# ---

# New data
newdata <- data.frame(z1 = 1,   z2 = 1,   y  = 1,   g  = 1,
                      w1 = 0.1, w2 = 0.2, w3 = 0.3, w4 = 0.4, w5 = 0.5)

# Predict unconditional expectation of the dependent variable
  # regime 0 for 'y' and regime 1 for 'g' i.e. E(y0 | w), E(g1 | w)
predict(model, group = c(-1, -1), group2 = c(0, 1), newdata = newdata)

# Predict conditional expectations of the dependent variable
# E(y0 | z1 = 2, z2 = 1, w), E(g1 | z1 = 2, z2 = 1, w)
predict(model, group = c(2, 1), group2 = c(0, 1), newdata = newdata)

# Marginal effect of w3 on 
# E(y1 | z1 = 2, z2 = 1, w) and E(g1 | z1 = 2, z2 = 1, w)
predict(model, group   = c(2, 1), group2 = c(0, 1), 
               newdata = newdata, me     = "w3")

# ---
# Step 4
# Two-step estimation procedure
# ---

# Provide manually selectivity terms
model2 <- msel(list(z1 ~ w1 + w2 | w2 + w3, z2 ~ w1 + w3),
               list(y ~ w1 + w3 + w4 + 
                        lambda1 + lambda2 + I(lambda1 * lambda2), 
                    g ~ w2 + w3 + w5 + lambda1 + lambda2),
               groups = groups, groups2   = groups2,
               data   = data,   estimator = "2step")
summary(model2)



# ---------------------------------------
# Simulated data example 8
# Multinomial endogenous switching and 
# selection model (probit)
# ---------------------------------------

# Load required package
library("mnorm")

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 10000

# Random errors
  # variances and correlations
sd.z2    <- sqrt(0.9)
cor.z    <- 0.3
sd.y0    <- sqrt(2)
cor.z1y0 <- 0.4
cor.z2y0 <- 0.7
sd.y1    <- sqrt(1.8)
cor.z1y1 <- 0.3
cor.z2y1 <- 0.6
cor.y    <- 0.8
  # the covariance matrix
sigma <- matrix(c(
1,                cor.z * sd.z2,            cor.z1y0 * sd.y0,         cor.z1y1 * sd.y1,
cor.z * sd.z2,    sd.z2 ^ 2,                cor.z2y0 * sd.z2 * sd.y0, cor.z2y1 * sd.z2 * sd.y1,
cor.z1y0 * sd.y0, cor.z2y0 * sd.z2 * sd.y0, sd.y0 ^ 2,                cor.y * sd.y0 * sd.y1,
cor.z1y1 * sd.y1, cor.z2y1 * sd.z2 * sd.y1, cor.y * sd.y0 * sd.y1,    sd.y1 ^ 2),
                ncol = 4, byrow = TRUE)
colnames(sigma) <- c("z1", "z2", "y0", "y1")
rownames(sigma) <- colnames(sigma)

# Simulate the random errors
errors <- rmnorm(n, c(0, 0, 0, 0), sigma)
u      <- errors[, 1:2]
eps    <- errors[, 3:4]

# Regressors (covariates)
x1 <- runif(n, -1, 1)
x2 <- runif(n, -1, 1)
x3 <- (x2 + runif(n, -1, 1)) / 2
W  <- cbind(1, x1, x2)
X  <- cbind(1, x1, x3)

# Coefficients
gamma0 <- c(0.1,    1,   1)
gamma1 <- c(0.2, -1.2, 0.8)
beta0  <- c(1,     -3,   4) 
beta1  <- c(1,      4,  -3)

# Linear predictors (indexes)
z1.li <- W %*% gamma0
z2.li <- W %*% gamma1
y0.li <- X %*% beta0
y1.li <- X %*% beta1

# Latent variables
z1.star <- z1.li + u[, 1]
z2.star <- z2.li + u[, 2]
y0.star <- y0.li + eps[, 1]
y1.star <- y1.li + eps[, 2]

# Obvservable variable as a dummy
z1 <- (z1.star > z2.star) & (z1.star > 0)
z2 <- (z2.star > z1.star) & (z2.star > 0)
z3 <- (z1 != 1) & (z2 != 1)

# Observable multinomial variable
z     <- rep(0, n)
z[z1] <- 0
z[z2] <- 1
z[z3] <- 2
table(z)

# Make unobservable values of the continuous outcome
y         <-  rep(NA, n)
y[z == 1] <- y0.star[z == 1]
y[z == 2] <- y1.star[z == 2]

# Data
data <- data.frame(z = z, y = y, x1 = x1, x2 = x2, x3 = x3)
            
# ---
# Step 2
# Estimation of the parameters
# ---

# Define the groups
groups3 <- c(0, 1, 2)
groups2 <- matrix(c(-1, 0, 1), ncol = 1)

# Two-step method
model <- msel(formula3  = z ~ x1 + x2, formula2 = y ~ x1 + x3, 
              groups3   = groups3,     groups2   = groups2,
              data      = data,        estimator = "2step",
              type3     = "probit")
summary(model)

# Compare estimates and true values of parameters
  # regression coefficients of the continuous equation
beta0_est <- coef(model, type = "coef2", eq2 = 1, regime = 0)
beta1_est <- coef(model, type = "coef2", eq2 = 1, regime = 1)
cbind(true = beta0, est = beta0_est[1:length(beta0)])
cbind(true = beta1, est = beta1_est[1:length(beta1)])
  # regression coefficients of the multinomial equations
gamma0_est <- coef(model, type = "coef3", eq3 = 0)
gamma1_est <- coef(model, type = "coef3", eq3 = 1)
cbind(true = gamma0, est = gamma0_est)
cbind(true = gamma1, est = gamma1_est)
  # compare the covariances between
  # z1 and z2
cbind(true = cor.z * sd.z2, 
      est  = coef(model, type = "cov3", eq3 = c(0, 1)))
  # z1 and y0
cbind(true = cor.z1y0 * sd.y0, 
      est  = beta0_est["lambda1_mn"]) 
  # z2 and y0
cbind(true = cor.z2y0 * sd.y0, 
      est  = beta0_est["lambda2_mn"])   
  # z1 and y1
cbind(true = cor.z1y1 * sd.y1, 
      est  = beta1_est["lambda1_mn"]) 
  # z2 and y1
cbind(true = cor.z2y1 * sd.y1, 
      est  = beta1_est["lambda2_mn"]) 
            
# ---
# Step 3
# Predictions and marginal effects
# ---  

# Unconditional expectation E(y1 | w) for every observation in a sample
predict(model, type = "val", group2 = 1, group3 = -1)

# Marginal effect of x1 on conditional expectation E(y0 | z = 1, w) 
# for every observation in a sample
predict(model, type = "val", group2 = 0, group3 = 1, me = "x1")    

# Calculate predictions and marginal effects
# for manually provided observations
# using aforementioned models.
newdata <- data.frame(z  = c(1, 1), 
                      y  = c(1, 1), 
                      x1 = c(0.5, 0.2), 
                      x2 = c(-0.3, 0.8),
                      x3 = c(0.6, -0.7))
                      
# Unconditional expectation E(y0 | w)
predict(model, type = "val", group2 = 0, group3 = -1, newdata = newdata)

# Conditional expectation E(y1 | z=2, w)
predict(model, type = "val", group2 = 1, group3 = 2, newdata = newdata)

# Marginal effect of x2 on E(y0 | z = 1, w)
predict(model, type = "val", group2  = 0, group3 = 1, 
               me   = "x2",  newdata = newdata)
  
# ---
# Step 4
# Multinomial logit selection
# ---                
        
# Two-step method
model2 <- msel(formula3  = z ~ x1 + x2, formula2 = y ~ x1 + x3, 
               groups3   = groups3,     groups2   = groups2,
               data      = data,        estimator = "2step",
               type3     = "logit")
summary(model2)    

# Compare the estimates
beta0_est2 <- coef(model2, type = "coef2", eq2 = 1, regime = 0)[]
beta1_est2 <- coef(model2, type = "coef2", eq2 = 1, regime = 1)

# beta0 coefficients
cbind(true = beta0, probit = beta0_est[1:3], logit = beta0_est2[1:3])
   
# beta1 coefficients
cbind(true = beta1, probit = beta1_est[1:3], logit = beta1_est2[1:3])            

             

# ---------------------------------------
# Simulated data example 9
# Multinomial endogenous switching and 
# selection model (logit)
# ---------------------------------------

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# The number of observations
n <- 10000

# Random errors
u      <- matrix(-log(-log(runif(n * 3))), nrow = n, ncol = 3)
tau0   <- matrix(c(0.6, -0.4, 0.3), ncol = 1)
tau1   <- matrix(c(-0.3, 0.5, 0.2), ncol = 1)
eps0   <- (u - 0.57721656649) %*% tau0 + rnorm(n)
eps1   <- (u - 0.57721656649) %*% tau1 + rnorm(n)

# Regressors (covariates)
x1 <- runif(n = n, min = -1, max = 1)
x2 <- runif(n = n, min = -1, max = 1)
x3 <- runif(n = n, min = -1, max = 1)

# Coefficients
gamma.0 <- c(0.2, -2,  2)
gamma.1 <- c(0.1,  2, -2)
beta.0  <- c(2,    2,  2)
beta.1  <- c(1,   -2,  2)

# Linear predictors (indexes)
z0.li <- gamma.0[1] + gamma.0[2] * x1 + gamma.0[3] * x2
z1.li <- gamma.1[1] + gamma.1[2] * x1 + gamma.1[3] * x2

# Latent variables
z0.star <- z0.li + u[, 1]
z1.star <- z1.li + u[, 2]
z2.star <- u[, 3]
y0.star <- beta.0[1] + beta.0[2] * x1 + beta.0[3] * x3 + eps0
y1.star <- beta.1[1] + beta.1[2] * x1 + beta.1[3] * x3 + eps1

# Observable multinomial variable
z                                            <- rep(2, n)
z[(z0.star > z1.star) & (z0.star > z2.star)] <- 0
z[(z1.star > z0.star) & (z1.star > z2.star)] <- 1
table(z)

# Unobservable values of the continuous outcome
y         <- rep(NA, n)
y[z == 0] <- y0.star[z == 0]
y[z == 1] <- y1.star[z == 1]

# Data
data <- data.frame(x1 = x1, x2 = x2, x3 = x3, z = z, y = y)
            
# ---
# Step 2
# Estimation of the parameters
# ---

# Define the groups
groups3 <- c(0, 1, 2)
groups2 <- c(0, 1, -1)

# Two-step estimator of Dubin-McFadden
model <- msel(formula3  = z ~ x1 + x2, formula2 = y ~ x1 + x3, 
              groups3   = groups3,     groups2   = groups2,
              data      = data,        estimator = "2step",
              type3     = "logit")
summary(model)

# Least squaes estimates (benchmark)
lm0 <- lm(y ~ x1 + x3, data = data[data$z == 0, ])
lm1 <- lm(y ~ x1 + x3, data = data[data$z == 1, ])


# Compare the estimates of beta0
cbind(true = beta.0,
      DMF  = coef(model, type = "coef2", eq2 = 1, regime = 0),
      LS   = coef(lm0))
      
# Compare the estimates of beta1
cbind(true = beta.1,
      DMF  = coef(model, type = "coef2", eq2 = 1, regime = 1),
      LS   = coef(lm1))

Extract the Number of Observations from a Fit of the msel Function.

Description

Extract the number of observations from a model fit of the msel function.

Usage

## S3 method for class 'msel'
nobs(object, ...)
## S3 method for class 'msel'
nobs(object, ...)

Arguments

`object`	object of class "msel"
`...`	further arguments (currently ignored)

Details

Unobservable values of continuous equations are included into the number of observations.

Value

A single positive integer number.

Predict method for msel function

Description

Predicted values based on the object of class 'msel'.

Usage

## S3 method for class 'msel'
predict(
  object,
  ...,
  newdata = NULL,
  given_ind = numeric(),
  group = NA,
  group2 = NA,
  group3 = NA,
  type = ifelse(any(is.na(group2)), "prob", "val"),
  me = NULL,
  eps = NULL,
  control = list(),
  test = FALSE,
  exogenous = NULL
)
## S3 method for class 'msel'
predict(
  object,
  ...,
  newdata = NULL,
  given_ind = numeric(),
  group = NA,
  group2 = NA,
  group3 = NA,
  type = ifelse(any(is.na(group2)), "prob", "val"),
  me = NULL,
  eps = NULL,
  control = list(),
  test = FALSE,
  exogenous = NULL
)

Arguments

`object`	an object of class "msel".
`...`	further arguments (currently ignored)
`newdata`	an optional data frame in which to look for variables with which to predict. If omitted, the original data frame used. This data frame should contain values of dependent variables even if they are not actually needed for prediction (simply assign them with 0 values).
`given_ind`	a numeric vector of indexes of conditioned components.
`group`	a numeric vector which i-th element represents a value of the i-th dependent variable. If this value equals -1 then this component will be ignored (useful for estimation of marginal probabilities).
`group2`	a numeric vector which i-th element represents a value of the i-th dependent variable of the continuous equation. If this value equals -1 then this component will be ignored.
`group3`	an integer representing the index of the alternative of the multinomial equation. If this value equals -1 then this component will be ignored.
`type`	a string representing a type of the prediction. See 'Details' for more information.
`me`	a string representing the name of the variable for which marginal effect should be estimated. See 'Details' for more information.
`eps`	a numeric vector of length 1 or 2 used for calculation of the marginal effects. See 'Details' for more information.
`control`	a list of additional arguments. Currently is not intended for the users.
`test`	a logical, function or integer. If `test = TRUE` then the output of the function is supplied to `test_msel` before return to perform a t-test. If `test` is a function it will be applied to the output of the `predict` before `test_msel` is called. If `test` is an integer then `test_msel` will be applied only to the `test`-th column of the output.
`exogenous`	a list such that `exogenous[[i]]` represents the value (or a vector of values of the same size as `nrow(newdata)`) which will be exogenously assigned to the variable `names(exogenous)[[i]]` in `newdata` i.e., `newdata[, names(exogenous)[i]] <- exogenous[[i]]`. If `newdata` is `NULL` and `exogenous` is not `NULL` then `newdata` is set to `object$data`. This argument is especially useful for the casual inference when some endogenous (dependent) variables should be exogenously assigned with some values i.e., in the right hand side of the `formula`, `formula2` and `formula3`. The purpose of `exogeneous` argument is just a convenience so equivalently it is possible to exogenously provide the values to variables via the `newdata` argument.

Details

See 'Examples' section of msel for the examples of this function application.

Probabilities of the multivariate ordinal equations

If type = "prob" then the function returns a joint probability that the ordinal outcomes will have values assigned in group. To calculate marginal probabilities set unnecessary group values to -1.

To estimate conditional probabilities provide indexes of the conditioned outcomes through the given_ind argument.

For example, if $z_{1i}$ , $z_{2i}$ and $z_{3i}$ are the ordinal outcomes then to estimate $P(z_{1i}=2 | z_{3i} = 0, w_{1i}, w_{2i}, w_{3i})$ set given_ind = 3 and groups = c(2, -1, 0).

Linear predictors (indexes) of the multivariate ordinal equations

If type = "li" or type = "lp" then the function returns a matrix which columns are linear predictors (indexes) of the corresponding equations. If group[j] = -1 then linear predictors (indexes) associated with the j-th ordinal equation will be omitted from the output.

For example, if group = c(0, -1, 1) then the function returns a matrix which first column is $w_{1i}\hat{\gamma}_{1}$ and the second column is $w_{3i}\hat{\gamma}_{3}$ .

Standard deviations of the multivariate ordinal equations

If type = "sd" then the function returns a matrix which columns are the estimates of the standard deviations of the random errors for the corresponding equations. If group[j] = -1 then the standard deviations associated with the j-th ordinal equation will be omitted from the output.

For example, if group = c(0, -1, 1) then the function returns a matrix which first column is $\hat{\sigma}_{1i}^{*}$ and the second column is $\hat{\sigma}_{3i}^{*}$ .

Predictions of the continuous outcomes

If type = "val" then the function returns the predictions of the conditional (on group) expectation of the continuous outcomes in the regimes determined by the group2 argument. To predict unconditional expectations set group to a vector of -1 values.

For example, suppose that there is a single continuous equation $y_{i}$ and two ordinal equations $z_{1i}$ and $z_{2i}$ . To estimate $E(y_{2i}|x_{i})$ set group = c(-1, -1) and group2 = 2. To estimate $E(y_{1i}|x_{i}, z_{1i} = 2, z_{2i} = 0)$ set group = c(2, 0) and group2 = 1. To estimate $E(y_{0i}|x_{i}, z_{2i} = 1)$ set group = c(-1, 1) and group2 = 0.

Suppose that there are two continuous $y_{i}^{(1)}$ , $y_{i}^{(2)}$ and two ordinal $z_{1i}$ , $z_{2i}$ equations. If group2 = c(1, 3) and group = c(3, 0) then the function returns a matrix which first column are the estimates of $E(y_{1i}^{(1)}|z_{1i} = 3, z_{2i} = 0, x_{i}^{(1)})$ and the second column are the estimates of $E(y_{3i}^{(2)}|z_{1i} = 3, z_{2i} = 0, x_{i}^{(2)})$ .

Selectivity terms

If type = "lambda" then the function returns a matrix which j-th column is a numeric vector of estimates of the selectivity terms $\lambda_{ji}$ associated with the ordinal equations. Similarly if type = "lambda_mn" then the function returns a numeric matrix with the selectivity terms of the multinomial equations.

Probabilities of the multinomial equation

If type = "prob_mn" and group3 = j then the function returns a vector of the estimates of the probabilities $P(\tilde{z}_{i}=j|\tilde{w}_{i})$ .

Linear indexes (predictors) of the multinomial equation

If type = "li_mn" or type = "lp_mn" then the function returns a numeric matrix which j-th column is a numeric vector of estimates of the linear predictor (index) associated with the (j-1)-th alternative $\tilde{w}_{i}\tilde{\gamma}_{(j-1)}$ .

Estimation of the marginal effects

If me is provided then the function returns marginal effect of variable me respect to the statistic determined by the type argument.

For example, if me = "x1" and type = "prob" then the function returns a marginal effect of x1 on the corresponding probability i.e., one that would be estimated if me is NULL.

If length(eps) = 1 then eps is an increment in numeric differentiation procedure. If eps is NULL then this increment will be selected automatically taking into account scaling of variables. If length(eps) = 2 then marginal effects will be estimated as the difference of predicted value when variable me equals eps[2] and eps[1] correspondingly.

For example, suppose that type = "prob", me = "x1", given_ind = 3 and groups = c(2, -1, 0). Then if eps is a NULL or a small number (something like eps = 0.0001) then the following marginal effect will be estimated (via the numeric differentiation):

$\frac{\partial P(z_{1i}=2 | z_{3i} = 0)}{\partial x_{1i}}.$

If eps = c(1, 3) then the function estimates the following difference (useful for estimation of marginal effects of ordered covariates):

$P(z_{1i}=2 | z_{3i} = 0, x_{1i} = 3) - P(z_{1i}=2 | z_{3i} = 0, x_{1i} = 1).$

Notice that the conditioning on $w_{ji}$ has been omitted for brevity.

Causal inference

Argument exogenous is useful for the causal inference. For example, suppose that there are two binary outcomes $z_{1i}$ and $z_{2i}$ . Also $z_{1i}$ is the endogenous regressor for $z_{2i}$ . That is $z_{1i}$ appears both on the left hand side of formula[[1]] and on the right hand side of formula[[2]]. Consider the estimation of the average treatment effect:

$ATE = P(z_{2i} = 1|do(z_{1i}) = 1) - P(z_{2i} = 1|do(z_{1i}) = 0),$

where $do$ is a do-calculus operator. The estimate of the average treatment effect is as follows:

$\widehat{ATE} = \frac{1}{n}\sum\limits_{i=1}^{n}p_{1i}-p_{0i},$

where:

$p_{1i} = \hat{P}(z_{2i} = 1|do(z_{1i}) = 1, w_{1i}, w_{2i}^{(*)}),$

$p_{0i} = \hat{P}(z_{2i} = 1|do(z_{1i}) = 0, w_{1i}, w_{2i}^{(*)}).$

Vector $w_{2i}^{(*)}$ denotes all the regressors $w_{2i}$ except the endogenous one $z_{1i}$ .

To get $\widehat{ATE}$ it is sufficient to make the following steps. First, calculate $p_{1i}$ by setting type = "prob", group = c(-1, 1) and providing the value 1 to $z_{1i}$ through the exogenous argument. Second, calculate $p_{0i}$ by setting type = "prob", group = c(-1, 0) and providing the value 0 to $z_{1i}$ through the exogenous argument. Third, take the average value of $p_{1i}-p_{0i}$ .

Value

This function returns predictions for each row of newdata or for each observation in the model if newdata is NULL. Structure of the output depends on the type argument (see 'Details' section).

Print Method for Likelihood Ratio Test

Description

Prints summary for an object of class 'lrtest_msel'.

Usage

## S3 method for class 'lrtest_msel'
print(x, ...)
## S3 method for class 'lrtest_msel'
print(x, ...)

Arguments

`x`	object of class "lrtest_msel".
`...`	further arguments (currently ignored).

Value

The function returns the input argument x.

Print for an Object of Class msel

Description

Prints information on the object of class 'msel'.

Usage

## S3 method for class 'msel'
print(x, ...)
## S3 method for class 'msel'
print(x, ...)

Arguments

`x`	object of class 'msel'
`...`	further arguments (currently ignored)

Value

The function returns NULL.

Print for an Object of Class struct_msel

Description

Prints information on the object of class 'struct_msel'.

Usage

## S3 method for class 'struct_msel'
print(x, ...)
## S3 method for class 'struct_msel'
print(x, ...)

Arguments

`x`	object of class 'struct_msel'
`...`	further arguments (currently ignored)

Value

The function returns NULL.

Print Summary Method for Likelihood Ratio Test

Description

Prints summary for an object of class 'lrtest_msel'.

Usage

## S3 method for class 'summary.lrtest_msel'
print(x, ...)
## S3 method for class 'summary.lrtest_msel'
print(x, ...)

Arguments

`x`	object of class "lrtest_msel"
`...`	further arguments (currently ignored)

Value

The function returns input argument x changing it's class to lrtest_msel.

Print summary for an Object of Class msel

Description

Prints summary for an object of class 'msel'.

Usage

## S3 method for class 'summary.msel'
print(x, ...)
## S3 method for class 'summary.msel'
print(x, ...)

Arguments

`x`	object of class 'msel'
`...`	further arguments (currently ignored)

Value

The function returns x.

Print summary for an Object of Class test_msel

Description

Prints summary for an object of class 'test_msel'.

Usage

## S3 method for class 'summary.test_msel'
print(x, ..., is_legend = TRUE)
## S3 method for class 'summary.test_msel'
print(x, ..., is_legend = TRUE)

Arguments

`x`	object of class 'test_msel'
`...`	further arguments (currently ignored)
`is_legend`	a logical; if `TRUE` then additional information is shown.

Value

The function returns input argument x.

Extract Residual Standard Deviation 'Sigma'

Description

Extract standard deviations of random errors of continuous equations of msel function.

Usage

## S3 method for class 'msel'
sigma(object, use.fallback = TRUE, ..., regime = NULL, eq2 = NULL)
## S3 method for class 'msel'
sigma(object, use.fallback = TRUE, ..., regime = NULL, eq2 = NULL)

Arguments

`object`	object of class "msel".
`use.fallback`	logical, passed to `nobs` (currently ignored).
`...`	further arguments (currently ignored).
`regime`	regime of continuous equation
`eq2`	index of continuous equation

Details

Available only if estimator = "ml" or all degrees values are equal to 1.

Value

Returns estimates of the standard deviations of $\varepsilon_{i}$ . If eq2 = k then estimates only for $k$ -th continuous equation are returned. If in addition regime = r then estimate of $\sqrt{Var(\varepsilon_{ri})}$ is returned. Herewith if regime is not NULL and eq2 is NULL it is assumed that eq2 = 1.

Stars for p-values

Description

This function assigns stars (associated with different significance levels) to p-values.

Usage

starsVector(p_value)
starsVector(p_value)

Arguments

p_value

vector of values between 0 and 1 representing p-values.

Details

Three stars are assigned to p-values not greater than 0.01. Two stars are assigned to p-values greater than 0.01 and not greater than 0.05. One star is assigned to p-values greater than 0.05 and not greater than 0.1.

Value

The function returns a string vector of stars assigned according to the rules described in 'Details' section.

Examples

p_value <- c(0.002, 0.2, 0.03, 0.08)
starsVector(p_value)
p_value <- c(0.002, 0.2, 0.03, 0.08)
starsVector(p_value)

Structure of the Object of Class msel

Description

Prints information on the structure of the model.

Usage

struct_msel(x)
struct_msel(x)

Arguments

`x`	object of class 'msel'

Value

The function returns a numeric matrix which columns are groups, groups2, groups3 correspondingly. It also has additional (last) column with the number of observations associated with the corresponding combinations of the groups.

Summary Method for Likelihood Ratio Test

Description

Provides summary for an object of class 'lrtest_msel'.

Usage

## S3 method for class 'lrtest_msel'
summary(object, ...)
## S3 method for class 'lrtest_msel'
summary(object, ...)

Arguments

`object`	object of class "lrtest_msel"
`...`	further arguments (currently ignored)

Details

This function just changes the class of the 'lrtest_msel' object to 'summary.lrtest_msel'.

Value

Returns an object of class 'summary.lrtest_msel'.

Summary for an Object of Class msel

Description

Provides summary for an object of class 'msel'.

Usage

## S3 method for class 'msel'
summary(object, ..., vcov = NULL, show_ind = FALSE)
## S3 method for class 'msel'
summary(object, ..., vcov = NULL, show_ind = FALSE)

Arguments

`object`	object of class 'msel'
`...`	further arguments (currently ignored)
`vcov`	positively defined numeric matrix representing asymptotic variance-covariance matrix of the estimator to be used for calculation of standard errors and p-values. It may also be a character. Then `vcov.msel` function will be used which input argument `type` will be set to `vcov`. If `estimator = "2step"` then `vcov` should be an estimate of the asymptotic covariance matrix of the first step estimator.
`show_ind`	logical; if `TRUE` then indexes of parameters will be shown. Particularly, these indexes may be used in `ind` element of `regularization` parameter of `msel`.

Details

If vcov is NULL then this function just changes the class of the 'msel' object to 'summary.msel'. Otherwise it additionally changes object$cov to vcov and use it to recalculate object$se, object$p_value and object$tbl values. It also adds the value of ind argument to the object.

Value

Returns an object of class 'summary.msel'.

Summary for an Object of Class delta_method

Description

Provides summary for an object of class 'delta_method'.

Usage

## S3 method for class 'test_msel'
summary(object, ..., is_legend = TRUE)
## S3 method for class 'test_msel'
summary(object, ..., is_legend = TRUE)

Arguments

`object`	object of class 'delta_method'
`...`	further arguments (currently ignored)
`is_legend`	a logical; if `TRUE` then additional information is shown.

Value

Returns an object of class 'summary.delta_method'.

Tests and confidence intervals for the parameters estimated by the msel function

Description

This function conducts various statistical tests and calculates confidence intervals for the parameters of the model estimated via the msel function.

Usage

test_msel(
  object,
  fn,
  fn_args = list(),
  test = "t",
  method = "classic",
  ci = "classic",
  cl = 0.95,
  se_type = "dm",
  trim = 0,
  vcov = object$cov,
  iter = 100,
  generator = rnorm,
  bootstrap = NULL,
  par_ind = 1:object$control_lnL$n_par,
  eps = max(1e-04, sqrt(.Machine$double.eps) * 10),
  n_sim = 1000,
  n_cores = 1
)
test_msel(
  object,
  fn,
  fn_args = list(),
  test = "t",
  method = "classic",
  ci = "classic",
  cl = 0.95,
  se_type = "dm",
  trim = 0,
  vcov = object$cov,
  iter = 100,
  generator = rnorm,
  bootstrap = NULL,
  par_ind = 1:object$control_lnL$n_par,
  eps = max(1e-04, sqrt(.Machine$double.eps) * 10),
  n_sim = 1000,
  n_cores = 1
)

Arguments

`object`	an object of class 'msel'. It also may be a list of two objects. Then `object[[1]]` and `object[[2]]` are supplied to the arguments `model1` and `model2` of the `lrtest_msel` function.
`fn`	a function which returns a numeric vector and should depend on the elements of `object`. These elements should be accessed via `coef.msel` or `predict.msel` functions. The first argument of `fn` should be an `object`. Therefore `coef` and `predict` functions in `fn` should also depend on `object`.
`fn_args`	a list of additional arguments of `fn`.
`test`	a character representing the test to be used. If `test = "t"` then t-test is used. If `test = "wald"` then Wald test is applied.
`method`	a character representing a method used to conduct a test. If `test = "t"` or `test = "wald"` and `method = "classic"` then p-values are calculated by using the quantiles of the standard normal distribution. If `test = "t"` or `test = "wald"` and `method = "bootstrap"` then p-values are calculated by using the bootstrap as described in Hansen (2022). If `test = "wald"` and `method = "score"` then score bootstrap Wald test of P. Kline and A. Santos (2012) is used.
`ci`	a character representing the type of the confidence interval used. Available only if `test = "t"`. If `ci = "classic"` then quantiles of the standard normal distribution are used to build an asymptotic confidence interval. If `ci = "percentile"` then percentile bootstrap interval is applied. If `ci = "bc"` then the function constructs a bias-corrected percentile bootstrap confidence interval of Efron (1982) as described in Hansen (2022).
`cl`	a numeric value between `0` and `1` representing a confidence level of the confidence interval.
`se_type`	a character representing a method used to estimate the standard errors of the outputs of `fn`. If `se_type = "dm"` then delta method is used. If `se_type = "bootstrap"` then bootstrap is applied.
`trim`	a numeric value between `0` and `1` representing the share of bootstrap estimates to be nullified when standard errors are estimated for `se_type = "bootstrap"`.
`vcov`	an estimate of the asymptotic covariance matrix of the parameters of the model.
`iter`	the number of iterations used by the score bootstrap Wald test.
`generator`	function which is used by the score bootstrap to generate random weights. It should have an argument `n` representing the number of random weights to generate. Other arguments are ignored.
`bootstrap`	an object of class `'bootstrap_msel'` which is an output of the `bootstrap_msel` function. This object is used to retrieve the estimates of the bootstrap samples.
`par_ind`	a vector of indexes of the model parameters used in the calculation of `fn`. If only necessary indexes are included then in some cases estimation time may greatly decrease. Set `ind = TRUE` in `summary.msel` to see the indexes of the model parameters. If `eps` is a vector then `eps[i]` determines the increment used to differentiate `fn` respect to the parameter with `par_ind[i]`-th index.
`eps`	a positive numeric value representing the increment used for the numeric differentiation of `fn`. It may also be a numeric vector such that `eps[i]` is an increment used to differentiate the `fn` respect to the `par_ind[i]`-th parameter of the model. Set `ind = TRUE` in `summary.msel`, to see the indexes of the model parameters. If `eps[i] = 0` then derivative of `fn` respect to `par_ind[i]`-th parameter is assumed to be zero.
`n_sim`	the value passed to the `n_sim` argument of the `msel` function.
`n_cores`	the value passed to the `n_cores` argument of the `msel` function.

Details

Suppose that $\theta$ is a vector of parameters of the model estimated via the msel function and $g(\theta)$ is a differentiable function representing fn which returns a $m$ -dimensional vector of real values:

$g(\theta) = (g_{1}(\theta),...,g_{m}(\theta))^{T}.$

Classic and bootstrap t-test

If test = "t" then for each $j\in \{1,...,m\}$ the following hypotheses is tested:

$H_{0}:g_{j}(\theta) = 0,\qquad H_{1}:g_{j}(\theta)\ne 0.$

The test statistic is:

$T = g_{j}(\hat{\theta})/\hat{\sigma}_{j},$

where $\hat{\sigma}$ is a standard error of $g_{j}(\hat{\theta})$ .

If se_type = "dm" then delta method is used to estimate this standard error:

$\hat{\sigma}_{j} = \sqrt{\nabla g_{j}(\hat{\theta})^{T} \widehat{As.Cov}(\hat{\theta}) \nabla g_{j}(\hat{\theta})},$

where $\nabla g_{j}(\hat{\theta})$ is a gradient as a column vector and the estimate of the asymptotic covariance matrix of the estimates $\widehat{As.Cov}(\hat{\theta})$ is provided via the vcov argument. Numeric differentiation is used to calculate $\nabla g_{j}(\hat{\theta})$ .

If se_type = "bootstrap" then bootstrap is applied to estimate the standard error:

$\hat{\sigma}_{j} = \sqrt{\frac{1}{B - 1}\sum\limits_{b = 1}^{B} (g_{j}(\hat{\theta}^{(b)}) - \overline{g_{j}(\hat{\theta}^{(b)}}))^2},$

where $B$ is the number of the bootstrap iterations bootstrap$iter, $\hat{\theta}^{(b)}$ is the estimate associated with the $b$ -th of these iterations bootstrap$par[b, ], and $g_{j}(\overline{\hat{\theta}^{(b)}})$ is a sample mean of the bootstrap estimates:

$\overline{g_{j}(\hat{\theta}^{(b)}}) = \frac{1}{B}\sum\limits_{b = 1}^{B}g_{j}(\hat{\theta}^{(b)}).$

If method = "classic" it is assumed that if the null hypothesis is true then the asymptotic distribution of the test statistic is standard normal. This distribution is used for the calculation of the p-value:

$p-value = 2\min(\Phi(T), 1 - \Phi(T)),$

where $\Phi()$ is a cumulative distribution function of the standard normal distribution.

If method = "bootstrap" then p-value is calculated via the bootstrap as suggested by Hansen (2022):

$p-value = \frac{1}{B}\sum\limits_{b=1}^{B} I(|T_{b} - T| > |T|),$

where $T_{b} = g_{j}(\hat{\theta}^{(b)})/\hat{\sigma}_{j}$ is the value of the test statistic estimated on the b-th bootstrap sample and $I(q)$ is an indicator function which equals $1$ when $q$ is a true statement and $0$ - otherwise.

Classic and bootstrap Wald test

Suppose that method = "classic" or method = "bootstrap". If test = "wald" then the null hypothesis is:

$H_{0}: \begin{cases} g_{1}(\theta) = 0\\ g_{2}(\theta) = 0\\ \vdots\\ g_{m}(\theta) = 0\\ \end{cases}.$

The alternative hypothesis is that there is such $j\in\{1,...,m\}$ that:

$H_{1}:g_{j}(\theta)\ne 0.$

The test statistic is:

$T = g(\hat{\theta})^{T}\widehat{As.Cov}(g(\hat{\theta}))^{-1} g(\hat{\theta}),$

where $\widehat{As.Cov}(g(\hat{\theta}))$ is the estimate of the asymptotic covariance matrix of $g(\hat{\theta})$ .

If se_type = "dm" then delta method is used to estimate this matrix:

$\widehat{As.Cov}(g(\hat{\theta})) = g'(\hat{\theta})\widehat{As.Cov}(\hat{\theta}) g'(\hat{\theta})^{T},$

where $g'(\hat{\theta})$ is a Jacobian matrix. A numeric differentiation is used to calculate its elements:

$g'(\hat{\theta})_{ij} = \frac{\partial g_{i}(\theta)}{\partial \theta_{j}}|_{\theta = \hat{\theta}}.$

If se_type = "bootstrap" then bootstrap is used to estimate this matrix:

$\widehat{As.Cov}(g(\hat{\theta}))= \frac{1}{B-1}\sum\limits_{i=1}^{B}q_{b}q_{b}^{T},$

where:

$q_{b} = (g(\hat{\theta}^{(b)}) - \overline{g(\hat{\theta}^{(b)})}),$

$\overline{g(\hat{\theta}^{(b)})} = \frac{1}{B}\sum\limits_{i=1}^{B} g(\hat{\theta}^{(b)}).$

If method = "classic" then it is assumed that if the null hypothesis is true then the asymptotic distribution of the test statistic is chi-squared with $m$ degrees of freedom. This distribution is used for the calculation of the p-value:

$p-value = 1 - F_{m}(T),$

where $F_{m}$ is a cumulative distribution function of the chi-squared distribution with $m$ degrees of freedom.

If method = "bootstrap" then p-value is calculated via the bootstrap as suggested by Hansen (2022):

$p-value = \frac{1}{B}\sum\limits_{b=1}^{B} I(T_{b} > T),$

where:

$T_{b} = s_{b}^{T}\widehat{As.Cov}(g(\hat{\theta}))^{-1}s_{b},$

$s_{b} = g(\hat{\theta}^{(b)}) - g(\hat{\theta}).$

Score bootstrap Wald test

If method = "score" and test = "Wald" then score bootstrap Wald test of Kline and Santos (2012) is used.

Consider $B$ independent samples of $n$ independent identically distributed random weights with zero mean and unit variance. Let $w_{ib}$ denote the $i$ -th weight of the $b$ -th sample. Argument generator is used to supply a function which generates these weights $w_{ib}$ and iter argument represents $B$ . Also $n$ is the number of observations in the model object$other$n_obs.

Let $J$ denote a matrix of sample scores object$J. Further, denote by $J_{b}$ a matrix such that its $b$ -th row is a product of the $w_{ib}$ and the $b$ -th row of $J$ . Also, denote by $H$ a matrix of mean values of the derivatives of sample scores respect to the estimated parameters object$H.

In addition consider the following notations:

$A = g'(\theta) H^{-1}, \qquad S_{b} = A J^{(c)}_{b},$

where $J^{(c)}_{b}$ is a vector of the column sums of $J_{b}$ .

The test statistic is as follows:

$T = g(\hat{\theta})^{T}(A\widehat{Cov}(J) A^{T})^{-1}g(\hat{\theta}) / n,$

where $\widehat{Cov}(J)$ is a sample covariance matrix of the sample scores of the model cov(object$J).

The test statistic on the $b$ -th bootstrap sample is similar:

$T_{b} = S^{T}(A\widehat{Cov}(J_{b}) A^{T})^{-1}S / n.$

The p-value is estimated as follows:

$p-value = \frac{1}{B}\sum\limits_{b=1}^{B} I(T_{b} \geq T).$

Confidence intervals

If test = "t" then the function also returns the realizations of the lower and upper bounds of the $100 \times$ cl percent symmetric asymptotic confidence interval of $g_{j}(\theta)$ .

If ci = "classic" then classic confidence interval is used which assumes asymptotic normality of $g_{j}(\hat{\theta})$ :

$(g_{j}(\hat{\theta}) + z_{(1 - cl) / 2}\hat{\sigma}_{j}, g_{j}(\hat{\theta}) + z_{1 - (1 - cl) / 2}\hat{\sigma}_{j}),$

where $z_{q}$ is a $q$ -th quantile of the standard normal distribution and $cl$ is a confidence level cl. The method used to estimate $\hat{\sigma}_{j}$ depends on the se_type argument as described above.

If ci = "percentile" then percentile bootstrap confidence interval is used. Therefore the sample quantiles of $g_{j}(\hat{\theta}^{(b)})$ are used as the realizations of the lower and upper bounds of the confidence interval.

If ci = "bc" then bias corrected percentile bootstrap confidence interval of Efron (1982) is used as described in Hansen (2022). The default percentile bootstrap confidence interval uses sample quantiles of levels $(1 - cl)/2$ and $1 - (1 - cl)/2$ . Bias corrected version uses the sample quantiles of the following levels:

$(1 - cl)/2 + \Phi(\Phi^{-1}((1 - cl)/2) + s),$

$1 - (1 - cl)/2 + \Phi(\Phi^{-1}(1 - (1 - cl)/2) + s),$

where:

$s = 2\Phi^{-1}(\frac{1}{B}\sum\limits_{b = 1}^{B} I(g_{j}(\hat{\theta}^{(b)})\leq g_{j}(\hat{\theta}))).$

Trimming

If se_type = "bootstrap" and trim > 0 then trimming is used as described in Hansen (2022) to estimate $\hat{\sigma}_{j}$ and $\widehat{As.Cov}(g(\hat{\theta}))$ . The algorithm is as follows. First, nullify 100trim percent of $g(\hat{\theta}^{(b)})$ with the greatest values of the L2-norm of $q_{b}$ (defined above). Then use this 'trimmed' sample to estimate the standard error and the asymptotic covariance matrix.

Value

This function returns an object of class 'test_msel' which is a list. It may have the following elements:

tbl - a list with the elements described below.
is_bootstrap - a logical value which equals TRUE if bootstrap has been used.
is_ci - a logical value which equals TRUE if confidence intervals were used.
test - the same as the input argument test.
method - the same as the input argument method.
se_type - the same as the input argument method.
ci - the same as the input argument ci.
cl - the same as the input argument cl.
iter - the same as the input argument iter.
n_bootstrap - an integer representing the number of the bootstrap iterations used.
n_val - the length of the vector returned by fn.

A list tbl may have the following elements:

val - an output of the fn function.
se - a numeric vector such that se[i] represents a standard error associated with val[i].
p_value - a numeric vector of p-values.
lwr - a numeric vector such that lwr[i] is the realization of the lower (left) bound of the confidence interval for the true value of val[i].
upr - a numeric vector such that upr[i] is the realization of the upper (right) bound of the confidence interval for the true value of val[i].
stat - a numeric vector of values of the test statistics.

An object of class 'test_msel' has an implementation of the summary method summary.test_msel.

In a special case when object is a list of length 2 the function returns an object of class 'lrtest_msel' since the function lrtest_msel is called internally.

References

B. Efron (1982). The Jackknife, the Bootstrap, and Other Resampling Plans. Society for Industrial and Applied Mathematics.

B. Hansen (2022). Econometrics. Princeton University Press.

P. Kline, A. Santos (2012). A Score Based Approach to Wild Bootstrap Inference. Journal of Econometric Methods, vol. 67, no. 1, pages 23-41.

Examples



# -------------------------------
# CPS data example
# -------------------------------

# Set seed for reproducibility
set.seed(123)

# Upload the data
data(cps)

# Estimate the employment model
model <- msel(work ~ age + I(age ^ 2) + bachelor + master, data = cps)
summary(model)    

# Use Wald test to test the hypothesis that age has no 
# effect on the conditional probability of employment:
# H0: coef age     = 0
#     coef age ^ 2 = 0
age_fn <- function(object)
{
  lwage_coef <- coef(object, type = "coef")[[1]]
  val        <- c(lwage_coef["age"], lwage_coef["I(age^2)"])
  return(val)
}
age_test <- test_msel(object = model, fn = age_fn, test = "wald")
summary(age_test)

# Use t-test to test for each individual the hypothesis:
# P(work = 1 | x) = 0.8
prob_fn <- function(object)
{
  prob <- predict(object, group = 1, type = "prob")
  val  <- prob - 0.8
  return(val)
}
prob_test <- test_msel(object = model, fn = prob_fn, test = "t")
summary(prob_test)

# -------------------------------
# Simulated data example
# Model with continuous outcome
# and ordinal selection
# -------------------------------

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# Load required package
library("mnorm")

# The number of observations
n <- 10000

# Regressors (covariates)
s1 <- runif(n = n, min = -1, max = 1)
s2 <- runif(n = n, min = -1, max = 1)
s3 <- runif(n = n, min = -1, max = 1)
s4 <- runif(n = n, min = -1, max = 1)

# Random errors
sigma <- matrix(c(1,    0.4,  0.45, 0.7,
                  0.4,  1,    0.54, 0.8,
                  0.45, 0.54, 0.81, 0.81,
                  0.7,  0.8,  0.81, 1), nrow = 4)
errors <- mnorm::rmnorm(n = n, mean = c(0, 0, 0, 0), sigma = sigma)
u1   <- errors[, 1]
u2   <- errors[, 2]
eps0 <- errors[, 3]
eps1 <- errors[, 4]

# Coefficients
gamma1     <- c(-1, 2)
gamma2     <- c(1, 1)
gamma1_het <- c(0.5, -1)
beta0      <- c(1, -1, 1, -1.2)
beta1      <- c(2, -1.5, 0.5, 1.2)
# Linear index of the ordinal equations
# mean part
li1 <- gamma1[1] * s1 + gamma1[2] * s2
li2 <- gamma2[1] * s1 + gamma2[2] * s3
# variance part
li1_het <- gamma1_het[1] * s2 + gamma1_het[2] * s3

# Linear index of the continuous equation
# regime 0
li_y0 <- beta0[1] + beta0[2] * s1 + beta0[3] * s3 + beta0[4] * s4
# regime 1
li_y1 <- beta1[1] + beta1[2] * s1 + beta1[3] * s3 + beta1[4] * s4

# Latent variables
z1_star <- li1 + u1 * exp(li1_het)
z2_star <- li2 + u2
y0_star <- li_y0 + eps0
y1_star <- li_y1 + eps1

# Cuts
cuts1 <- c(-1)
cuts2 <- c(0, 1)

# Observable ordinal outcome
# first
z1                     <- rep(0, n)
z1[z1_star > cuts1[1]] <- 1
# second
z2                                               <- rep(0, n)
z2[(z2_star > cuts2[1]) & (z2_star <= cuts2[2])] <- 1
z2[z2_star > cuts2[2]]                           <- 2
z2[z1 == 0]                                      <- NA

# Observable continuous outcome
y                 <- rep(NA, n)
y[which(z2 == 0)] <- y0_star[which(z2 == 0)]
y[which(z2 != 0)] <- y1_star[which(z2 != 0)]
y[which(z1 == 0)] <- NA

# Data
data <- data.frame(s1 = s1, s2 = s2, s3 = s3, s4 = s4,
                   z1 = z1, z2 = z2, y = y)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign the groups
groups  <- matrix(c(1, 2,
                    1, 1,
                    1, 0,
                    0, -1), 
                  byrow = TRUE, ncol = 2)
groups2 <- matrix(c(1, 1, 0, -1), ncol = 1)

# Estimate the model
model <- msel(list(z1 ~ s1 + s2 | s2 + s3,
                   z2 ~ s1 + s3),
              list(y  ~ s1 + s3 + s4),
              groups = groups, groups2 = groups2, 
              data   = data)
                   
# ---
# Step 3
# Hypotheses testing
# ---

# Use t-test to test for each observation the hypothesis
# H0: P(z1 = 0, z2 = 2 | Xi) = 0 
prob02_fn <- function(object)
{
   val <- predict(object, group = c(1, 0))
   
   return(val)
}
prob02_test <- test_msel(object = model, fn = prob02_fn, test = "t")
summary(prob02_test)

# Use t-test to test the hypothesis
# H0: E(y1|z1=0, z2=2) - E(y0|z1=0, z2=2)
ATE_fn <- function(object)
{
   val1 <- predict(object, group = c(0, 2), group2 = 1)
   val0 <- predict(object, group = c(0, 2), group2 = 0)
   val <- mean(val1 - val0)
   
   return(val)
}
ATE_test <- test_msel(object = model, fn = ATE_fn)
summary(ATE_test)

# Use Wald to test the hypothesis
# H0: beta1 = beta0
coef_fn <- function(object)
{
   coef1 <- coef(object, regime = 1, type = "coef2")
   coef0 <- coef(object, regime = 0, type = "coef2")
   coef_difference <- coef1 - coef0
   
   return(coef_difference)
}
coef_test <- test_msel(object = model, fn = coef_fn, test = "wald")
summary(coef_test)

# Use t-test to test for each 'k' the hypothesis
# H0: beta1k = beta0k
coef_test2 <- test_msel(object = model, fn = coef_fn, test = "t")
summary(coef_test2)

# Use Wald test to test the hypothesis
# H0: beta11 + beta12 - 0.5 = 0
#     beta11 * beta13 - beta03 = 0
test_fn <- function(object)
{
  coef1 <- coef(object, regime = 1, type = "coef2")
  coef0 <- coef(object, regime = 0, type = "coef2")
  val   <- c(coef1[1] + coef1[2] - 0.5, 
  coef1[1] * coef1[3] - coef0[3])
     
  return(val)
}
# classic Wald test
wald1 <- test_msel(object = model,  fn     = test_fn, 
                   test   = "wald", method = "classic")
summary(wald1)
# score bootstrap Wald test
wald2 <- test_msel(object = model,  fn     = test_fn, 
                   test   = "wald", method = "score")
summary(wald2)

# Replicate the latter test with the 2-step estimator
model2 <- msel(list(z1 ~ s1 + s2 | s2 + s3,
                         z2 ~ s1 + s3),
               list(y ~ s1 + s3 + s4),
               groups    = groups, groups2   = groups2, 
               data      = data,   estimator = "2step")
# classic Wald test
wald1_2step <- test_msel(object = model2, fn     = test_fn, 
                         test   = "wald", method = "classic")
summary(wald1_2step)
# score bootstrap Wald test
wald2_2step <- test_msel(object = model2, fn     = test_fn, 
                         test   = "wald", method = "score")
summary(wald2_2step)    

             
# -------------------------------
# CPS data example
# -------------------------------

# Set seed for reproducibility
set.seed(123)

# Upload the data
data(cps)

# Estimate the employment model
model <- msel(work ~ age + I(age ^ 2) + bachelor + master, data = cps)
summary(model)    

# Use Wald test to test the hypothesis that age has no 
# effect on the conditional probability of employment:
# H0: coef age     = 0
#     coef age ^ 2 = 0
age_fn <- function(object)
{
  lwage_coef <- coef(object, type = "coef")[[1]]
  val        <- c(lwage_coef["age"], lwage_coef["I(age^2)"])
  return(val)
}
age_test <- test_msel(object = model, fn = age_fn, test = "wald")
summary(age_test)

# Use t-test to test for each individual the hypothesis:
# P(work = 1 | x) = 0.8
prob_fn <- function(object)
{
  prob <- predict(object, group = 1, type = "prob")
  val  <- prob - 0.8
  return(val)
}
prob_test <- test_msel(object = model, fn = prob_fn, test = "t")
summary(prob_test)

# -------------------------------
# Simulated data example
# Model with continuous outcome
# and ordinal selection
# -------------------------------

# ---
# Step 1
# Simulation of the data
# ---

# Set seed for reproducibility
set.seed(123)

# Load required package
library("mnorm")

# The number of observations
n <- 10000

# Regressors (covariates)
s1 <- runif(n = n, min = -1, max = 1)
s2 <- runif(n = n, min = -1, max = 1)
s3 <- runif(n = n, min = -1, max = 1)
s4 <- runif(n = n, min = -1, max = 1)

# Random errors
sigma <- matrix(c(1,    0.4,  0.45, 0.7,
                  0.4,  1,    0.54, 0.8,
                  0.45, 0.54, 0.81, 0.81,
                  0.7,  0.8,  0.81, 1), nrow = 4)
errors <- mnorm::rmnorm(n = n, mean = c(0, 0, 0, 0), sigma = sigma)
u1   <- errors[, 1]
u2   <- errors[, 2]
eps0 <- errors[, 3]
eps1 <- errors[, 4]

# Coefficients
gamma1     <- c(-1, 2)
gamma2     <- c(1, 1)
gamma1_het <- c(0.5, -1)
beta0      <- c(1, -1, 1, -1.2)
beta1      <- c(2, -1.5, 0.5, 1.2)
# Linear index of the ordinal equations
# mean part
li1 <- gamma1[1] * s1 + gamma1[2] * s2
li2 <- gamma2[1] * s1 + gamma2[2] * s3
# variance part
li1_het <- gamma1_het[1] * s2 + gamma1_het[2] * s3

# Linear index of the continuous equation
# regime 0
li_y0 <- beta0[1] + beta0[2] * s1 + beta0[3] * s3 + beta0[4] * s4
# regime 1
li_y1 <- beta1[1] + beta1[2] * s1 + beta1[3] * s3 + beta1[4] * s4

# Latent variables
z1_star <- li1 + u1 * exp(li1_het)
z2_star <- li2 + u2
y0_star <- li_y0 + eps0
y1_star <- li_y1 + eps1

# Cuts
cuts1 <- c(-1)
cuts2 <- c(0, 1)

# Observable ordinal outcome
# first
z1                     <- rep(0, n)
z1[z1_star > cuts1[1]] <- 1
# second
z2                                               <- rep(0, n)
z2[(z2_star > cuts2[1]) & (z2_star <= cuts2[2])] <- 1
z2[z2_star > cuts2[2]]                           <- 2
z2[z1 == 0]                                      <- NA

# Observable continuous outcome
y                 <- rep(NA, n)
y[which(z2 == 0)] <- y0_star[which(z2 == 0)]
y[which(z2 != 0)] <- y1_star[which(z2 != 0)]
y[which(z1 == 0)] <- NA

# Data
data <- data.frame(s1 = s1, s2 = s2, s3 = s3, s4 = s4,
                   z1 = z1, z2 = z2, y = y)

# ---
# Step 2
# Estimation of the parameters
# ---

# Assign the groups
groups  <- matrix(c(1, 2,
                    1, 1,
                    1, 0,
                    0, -1), 
                  byrow = TRUE, ncol = 2)
groups2 <- matrix(c(1, 1, 0, -1), ncol = 1)

# Estimate the model
model <- msel(list(z1 ~ s1 + s2 | s2 + s3,
                   z2 ~ s1 + s3),
              list(y  ~ s1 + s3 + s4),
              groups = groups, groups2 = groups2, 
              data   = data)
                   
# ---
# Step 3
# Hypotheses testing
# ---

# Use t-test to test for each observation the hypothesis
# H0: P(z1 = 0, z2 = 2 | Xi) = 0 
prob02_fn <- function(object)
{
   val <- predict(object, group = c(1, 0))
   
   return(val)
}
prob02_test <- test_msel(object = model, fn = prob02_fn, test = "t")
summary(prob02_test)

# Use t-test to test the hypothesis
# H0: E(y1|z1=0, z2=2) - E(y0|z1=0, z2=2)
ATE_fn <- function(object)
{
   val1 <- predict(object, group = c(0, 2), group2 = 1)
   val0 <- predict(object, group = c(0, 2), group2 = 0)
   val <- mean(val1 - val0)
   
   return(val)
}
ATE_test <- test_msel(object = model, fn = ATE_fn)
summary(ATE_test)

# Use Wald to test the hypothesis
# H0: beta1 = beta0
coef_fn <- function(object)
{
   coef1 <- coef(object, regime = 1, type = "coef2")
   coef0 <- coef(object, regime = 0, type = "coef2")
   coef_difference <- coef1 - coef0
   
   return(coef_difference)
}
coef_test <- test_msel(object = model, fn = coef_fn, test = "wald")
summary(coef_test)

# Use t-test to test for each 'k' the hypothesis
# H0: beta1k = beta0k
coef_test2 <- test_msel(object = model, fn = coef_fn, test = "t")
summary(coef_test2)

# Use Wald test to test the hypothesis
# H0: beta11 + beta12 - 0.5 = 0
#     beta11 * beta13 - beta03 = 0
test_fn <- function(object)
{
  coef1 <- coef(object, regime = 1, type = "coef2")
  coef0 <- coef(object, regime = 0, type = "coef2")
  val   <- c(coef1[1] + coef1[2] - 0.5, 
  coef1[1] * coef1[3] - coef0[3])
     
  return(val)
}
# classic Wald test
wald1 <- test_msel(object = model,  fn     = test_fn, 
                   test   = "wald", method = "classic")
summary(wald1)
# score bootstrap Wald test
wald2 <- test_msel(object = model,  fn     = test_fn, 
                   test   = "wald", method = "score")
summary(wald2)

# Replicate the latter test with the 2-step estimator
model2 <- msel(list(z1 ~ s1 + s2 | s2 + s3,
                         z2 ~ s1 + s3),
               list(y ~ s1 + s3 + s4),
               groups    = groups, groups2   = groups2, 
               data      = data,   estimator = "2step")
# classic Wald test
wald1_2step <- test_msel(object = model2, fn     = test_fn, 
                         test   = "wald", method = "classic")
summary(wald1_2step)
# score bootstrap Wald test
wald2_2step <- test_msel(object = model2, fn     = test_fn, 
                         test   = "wald", method = "score")
summary(wald2_2step)

Update msel object with the new estimates

Description

This function updates parameters of the model estimated via msel function.

Usage

update_msel(object, par)
update_msel(object, par)

Arguments

`object`	an object of class `'msel'`.
`par`	a vector of parameters which substitutes `object$par` and used to update the estimates i.e., `object$coef`, `object$cuts` and others.

Details

It may be useful to apply this function to the bootstrap estimates of bootstrap_msel.

Value

This function returns an object object of class 'msel' in which object$par is substituted with par. Also, par is used to update the estimates i.e., object$coef, object$cuts and others.

Calculate Variance-Covariance Matrix for a msel Object.

Description

Return the variance-covariance matrix of the parameters of msel model.

Usage

## S3 method for class 'msel'
vcov(
  object,
  ...,
  type = object$cov_type,
  n_cores = object$other$n_cores,
  n_sim = object$other$n_sim,
  recalculate = FALSE
)
## S3 method for class 'msel'
vcov(
  object,
  ...,
  type = object$cov_type,
  n_cores = object$other$n_cores,
  n_sim = object$other$n_sim,
  recalculate = FALSE
)

Arguments

`object`	an object of class `msel`.
`...`	further arguments (currently ignored).
`type`	character representing the type of the asymptotic covariance matrix estimator. It takes the same values as `cov_type` parameter of the `msel` function.
`n_cores`	positive integer representing the number of CPU cores used for parallel computing. If possible it is highly recommend to set it equal to the number of available physical cores especially when the system of ordered equations has 2 or 3 equations.
`n_sim`	integer representing the number of GHK draws when there are more than 3 ordered equations. Otherwise alternative (much more efficient) algorithms will be used to calculate multivariate normal probabilities.
`recalculate`	logical; if `TRUE` then covariance matrix will be recalculated even if 'type' is the same as 'cov_type' input argument of the model.

Details

Argument type is closely related to the argument cov_type of msel function. See 'Details' and 'Usage' sections of msel for more information on cov_type argument.

Value

Returns numeric matrix which represents estimate of the asymptotic covariance matrix of model's parameters.

Package 'switchSelection'

Help Index

Bootstrap covariance matrix for least squares estimates of linear regression

Description

Usage

Arguments

Details

Value

Examples

Bootstrap for msel function

Description

Usage

Arguments

Details

Value

Examples

Coefficients extraction method for msel.

Description

Usage

Arguments

Details

Value

A subset of data from Current Population Survey (CPS).

Description

Usage

Format

Source

References

Examples

Modify exogenous variables in data frame

Description

Usage

Arguments

Details

Value

Extract Model Fitted Values

Description

Usage

Arguments

Value

Merge formulas

Description

Usage

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Details

Value

Examples

Split formula by symbol

Description

Usage

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Details

Value

Examples

Formulas of msel model.

Description

Usage

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Value

Gradient of the Log-likelihood Function of Multivariate Ordered Probit Model

Description

Usage

Arguments

Log-likelihood Function of Multivariate Ordered Probit Model

Description

Usage

Arguments

Extract Log-Likelihood from a Fit of the msel Function.

Description

Usage

Arguments

Details

Value

Leave-one-out cross-validation

Description

Usage

Arguments

Details

Value

Examples