ssmodels (version 2.0.1)

HeckmantS: Heckman-t Model Fit Function

Description

Fits a sample selection model based on the Student's t-distribution, extending the classical Heckman model to account for heavy-tailed error terms. The estimation is performed via Maximum Likelihood using the BFGS algorithm.

Usage

HeckmantS(selection, outcome, data = sys.frame(sys.parent()), df, start = NULL)

Value

A list containing:

• coefficients: Named vector of estimated model parameters.

• value: Negative of the maximum log-likelihood.

• loglik: Maximum log-likelihood.

• counts: Number of gradient evaluations performed.

• hessian: Hessian matrix at the optimum.

• fisher_infotS: Approximate Fisher information matrix.

• prop_sigmatS: Standard errors for the parameter estimates.

• level: Levels of the selection variable.

• nObs: Number of observations.

• nParam: Number of model parameters.

• N0: Number of censored (unobserved) observations.

• N1: Number of uncensored (observed) observations.

• NXS: Number of parameters in the selection equation.

• NXO: Number of parameters in the outcome equation.

• df: Degrees of freedom (observations minus parameters).

• aic: Akaike Information Criterion.

• bic: Bayesian Information Criterion.

• initial.value: Initial parameter values used in the optimization.

Arguments

selection

A formula specifying the selection equation.

outcome

A formula specifying the outcome equation.

data

A data frame containing the variables in the model.

df

Initial value for the degrees of freedom parameter of the t-distribution.

start

Optional numeric vector of initial parameter values.

Details

The function implements the Heckman sample selection model using the Student's t-distribution for the error terms, as proposed by marchenko2012heckman;textualssmodels. This extension allows for robustness against outliers and heavy-tailed distributions. Initial parameter values can be specified by the user or default to standard starting values.

References

marchenko2012heckmanssmodels

Examples

data(MEPS2001)
attach(MEPS2001)
selectEq <- dambexp ~ age + female + educ + blhisp + totchr + ins + income
outcomeEq <- lnambx ~ age + female + educ + blhisp + totchr + ins
HeckmantS(selectEq, outcomeEq, data = MEPS2001, df = 12)