HeckmantS {ssmodels} | R 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 Marchenko and Genton (2012). 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
Yulia V Marchenko, Marc G Genton (2012). “A Heckman selection-t model.” Journal of the American Statistical Association, 107(497), 304–317.
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)