Computation of the asymptotic variance matrix of the robust Heckman's two-stage estimator for truncated selection model.
heck2steprobVcov(y1vec, y2vec, x1Matr, x2Matr, eststage1, eststage2,
eststage2sigma, weights = rep(1,nrow(y1vec)), t.c = 1.345)vector of endogenous variables of the selection stage
vector of endogenous variables of the outcome stage
matrix of exogenous variables of the selection stage
matrix of exogenous variables of the outcome stage
object of class "glmrob", corresponding to the robust probit fit
vector of the coefficients of the outcome stage
the robust scale estimate of the second stage regression
vector of robustness weights
tuning constant of the second stage
Variance covariance matrix of the second stage estimator
The computation is made using the Huber (1967) - White (1980) sandwich estimator with Heckman (1979) correction. In the computation of leverage weights the lambda's are assumed to be fixed.
Heckman, J.J. (1979) Sample Selection Bias as a Specification Error. Econometrica, 47, p. 153-161.
Huber, P.J. (1967) The Behavior of Maximum Likelihood Estimates under Nonstandard Conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; L.M. LeCam, J. Neyman (Eds.), Berkeley: University of California Press, p. 221-233.
White, H.J. (1980) A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48, p. 817-838.