HeckmanBS {ssmodels}

Heckman BS Model fit Function

Description

Estimates the parameters of the Heckman-BS model

Usage

HeckmanBS(selection, outcome, data = sys.frame(sys.parent()), start = NULL)

Arguments

selection

Selection equation.

outcome

Primary Regression Equation.

data

Database.

start

initial values.

Details

The HeckmanBS() function fits the Sample Selection Model based on the Birnbaum–Saunders bivariate distribution, it has the same number of parameters as the classical Heckman model. For more information see Bastos and Barreto-Souza (2020)

Value

Returns a list with the following components.

Coefficients: Returns a numerical vector with the best estimated values of the model parameters;

Value: The value of function to be minimized (or maximized) corresponding to par.

loglik: Negative of value. Minimum (or maximum) of the likelihood function calculated from the estimated coefficients.

counts: Component of the Optim function. A two-element integer vector giving the number of calls to fn and gr respectively. This excludes those calls needed to compute the Hessian, if requested, and any calls to fn to compute a finite-difference approximation to the gradient.

hessian: Component of the Optim function, with pre-defined option hessian=TRUE. A symmetric matrix giving an estimate of the Hessian at the solution found. Note that this is the Hessian of the unconstrained problem even if the box constraints are active.

fisher_infoBS: Fisher information matrix

prop_sigmaBS: Square root of the Fisher information matrix diagonal

level: Selection variable levels

nObs: Numeric value representing the size of the database

nParam: Numerical value representing the number of model parameters

N0: Numerical value representing the number of unobserved entries

N1: Numerical value representing the number of complete entries

NXS: Numerical value representing the number of parameters of the selection model

NXO: Numerical value representing the number of parameters of the regression model

df: Numerical value that represents the difference between the size of the response vector of the selection equation and the number of model parameters

aic: Numerical value representing Akaike's information criterion.

bic: Numerical value representing Schwarz's Bayesian Criterion

initial.value: Numerical vector that represents the input values (Initial Values) used in the parameter estimation.

References

Fernando de Souza Bastos, Wagner Barreto-Souza (2020). “Birnbaum–Saunders sample selection model.” Journal of Applied Statistics.

Examples

data(MEPS2001)
attach(MEPS2001)
selectEq <- dambexp ~ age + female + educ + blhisp + totchr + ins + income
outcomeBS <- ambexp ~ age + female + educ + blhisp + totchr + ins
HeckmanBS(selectEq, outcomeBS, data = MEPS2001)