heckmanGE: A Package for Fitting Sample Selection Models

This package provides functions for fitting sample selection models, specifically the Heckman-Ge model. It includes functionality for specifying selection and outcome equations, as well as adjusting parameters for dispersion and correlation.

Estimates the parameters of the Generalized Heckman model

Usage

heckmanGE(
  selection,
  outcome,
  dispersion,
  correlation,
  data = sys.frame(sys.parent()),
  weights = NULL,
  cluster = NULL,
  start = NULL
)

Arguments

selection

A formula. Selection equation.

outcome

A formula. Outcome Equation.

dispersion

A right-handed formula. The equation for fitting of the Dispersion Parameter.

correlation

A right-handed formula. The equation for fitting of the Correlation Parameter.

data

A data.frame.

weights

an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector.

cluster

a variable indicating the clustering of observations, a list (or data.frame) thereof, or a formula specifying which variables from the fitted model should be used. See documentation for sandwich::vcovCL. A formula or list specifying the clusters for robust standard errors. Clustering adjusts the standard errors by accounting for correlations within clusters.

start

Optional. A numeric vector with the initial values for the parameters.

Value

A list of results from the fitted model, including parameter estimates, the Hessian matrix, number of observations, and other relevant statistics. If initial values are not provided, the function estimates them using the Heckman two-step method.

A list containing:

call

The matched function call.

coefficients

Estimated coefficients for the selection, outcome, dispersion, and correlation equations.

vcov

The covariance matrix of the estimated coefficients.

logLik

The log-likelihood of the fitted model.

model.frames

List of model frames for each equation (selection, outcome, dispersion, and correlation).

fitted.values

Fitted values of the outcome equation.

Details

Optimized Function for fitting the Generalized Heckman Model

(original version: package ssmodels. Modified by Rogerio Barbosa)

The heckmanGE() function fits a generalization of the Heckman sample selection model, allowing sample selection bias and dispersion parameters to depend on covariates. The heckmanGE() function fits a generalization of the Heckman sample selection model, and is compatible with robust variance-covariance estimation using packages such as sandwich. In particular, the vcovCL function can be used for clustering, which adjusts the standard errors by accounting for intra-cluster correlations in the data.

See also

vcovCL for computing robust standard errors with clustering. The function is compatible with the sandwich package for estimating heteroskedasticity-consistent and cluster-robust standard errors. This can be useful for adjusting the standard errors when dealing with grouped or clustered data. For more details, see the documentation for vcovCL.

Author

Fernando de Souza Bastos

Examples

data(MEPS2001)
selectEq  <- dambexp ~ age + female + educ + blhisp + totchr + ins + income
outcomeEq <- lnambx ~ age + female + educ + blhisp + totchr + ins
dispersion  <- ~ age + female + totchr + ins
correlation  <- ~ age
fit <- heckmanGE(selection = selectEq,
                 outcome = outcomeEq,
                 dispersion = dispersion,
                 correlation = correlation,
                 data = MEPS2001)
#> Error in eval(mfS): object 'selectEq' not found
summary(fit)
#> Error: object 'fit' not found