HeckmantS: Heckman-t Model Fit Function

View source: R/HeckmantS.R

HeckmantSR Documentation

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)

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 \insertCitemarchenko2012heckman;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.

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.

References

\insertRef

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)


ssmodels documentation built on June 8, 2025, 10:49 a.m.