Package that provides models to fit data with sample selection bias problems. Includes:
Heckman's classic model fit function. Sample selection usually arises in practice as a result of partial observability of the result of interest in a study. In the presence of sample selection, the observed data do not represent a random sample of the population, even after controlling for explanatory variables. That is, data is missing randomly. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze this data and proposed a complete likelihood estimation method under the assumption of normality. Such model was called Heckman model or Tobit 2 model.
Heckman-t model adjustment function. The Heckman-t model maintains the original parametric structure of the Classic Heckman model, but considers a bivariate Student's t distribution as the underlying joint distribution of the selection and primary regression variable and estimates the parameters by maximum likelihood.
Heckman-SK model adjustment function. The Heckman-sk model maintains the original parametric structure of the Classic Heckman model, but considers a bivariate Skew-Normal distribution as the underlying joint distribution of the selection and primary regression variable and estimates the parameters by maximum likelihood.
Heckman-BS model adjustment function. The Heckman-BS model maintains the original parametric structure of the Classic Heckman model, but considers a bivariate Birnbaum-Saunders distribution as the underlying joint distribution of the selection and primary regression variable and estimates the parameters by maximum likelihood.
Function for adjustment of Generalized Heckman model. The Generalized Heckman Model generalize the Classic Heckman model by adding covariables to the dispersion and correlation parameters, which allows to identify the covariates responsible for the presence of selection bias and the presence of heteroscedasticity.
Selection equation.
Primary Regression Equation.
Matrix with Covariables for fit of the Dispersion Parameter.
Matrix with Covariates for Adjusting the Correlation Parameter.
Initial value to the degree of freedom of Heckman-t model.
Initial value for asymmetry parameter.
initial values.
Database.
Applying any package function returns a list of results that include estimates of the fit model parameters, hessian matrix, number of observations, and more. If the initial value is not included in the function argument, an initial value is estimated from the Heckman two-step method setting.