fitheckmanGE: fitheckmanGE

In heckmanGE: Estimation and Inference for Heckman Selection Models with Cluster-Robust Variance

fitheckmanGE

Description

Newton-Raphson Optimization for Generalized Heckman Model Estimation

Usage

fitheckmanGE(start, YS, XS, YO, XO, Msigma, Mrho, w)

Arguments

start	A numeric vector of initial parameter guesses for the selection, outcome, dispersion, and correlation equations.
YS	A binary vector indicating selection status (1 if selected, 0 otherwise).
XS	A matrix of independent variables for the selection equation.
YO	A numeric vector of observed outcomes (dependent variable) for the outcome equation.
XO	A matrix of independent variables for the outcome equation.
Msigma	A matrix representing the predictors for the dispersion parameter.
Mrho	A matrix representing the predictors for the correlation parameter.
w	A numeric vector of observation weights, used in the likelihood computation.

Details

This function estimates the parameters of a generalized Heckman selection model using a Newton-Raphson optimization algorithm. It supports the modeling of selection and outcome equations, along with associated dispersion and correlation structures.

This function uses the Newton-Raphson algorithm to estimate the parameters of a generalized Heckman model, which accounts for sample selection bias. The model is composed of a selection equation (modeled by YS and XS), an outcome equation (modeled by YO and XO), and additional equations for dispersion (Msigma) and correlation (Mrho). The optimization process maximizes the log-likelihood of the model, allowing for robust estimation of selection bias, while also estimating associated dispersion and correlation parameters.

The function outputs the coefficients, fitted values, residuals, and several information criteria for model comparison.

Value

A list with the following components:

coefficients

Named vector of estimated coefficients for selection, outcome, dispersion, and correlation equations.

fitted.values

Named list with fitted values for each equation (selection, outcome, dispersion, correlation).

residuals

Numeric vector of residuals for the selection and outcome equations.

loglik

Log-likelihood value of the fitted model.

vcov

Variance-covariance matrix of the estimated parameters.

aic

Akaike Information Criterion (AIC) for the model.

bic

Bayesian Information Criterion (BIC) for the model.

optimization

Details of the optimization process, including convergence information.

heckmanGE documentation built on Oct. 7, 2024, 5:09 p.m.