heck5twosteprobVcov: Variance Covariance Matrix
In ssmrob: Robust Estimation and Inference in Sample Selection Models

Description Usage Arguments Details Value Author(s) References

Computation of the asymptotic variance matrix of the robust Heckman's two-stage estimator for the second regime of switching regression model, i.e. when y_1=0.

1 2	heck5twosteprobVcov(y1vec, y2vec, x1Matr, x2Matr, eststage1, eststage2, eststage2sigma, weights = rep(1,nrow(y1vec)), t.c = 1.345)

`y1vec`	vector of endogenous variables of the selection stage
`y2vec`	vector of endogenous variables of the outcome stage
`x1Matr`	matrix of exogenous variables of the selection stage
`x2Matr`	matrix of exogenous variables of the outcome stage
`eststage1`	object of class "`glmrob`", corresponding to the robust probit fit
`eststage2`	vector of the coefficients of the outcome stage
`eststage2sigma`	the robust scale estimate of the second stage regression
`weights`	vector of robustness weights
`t.c`	tuning constant of the second stage

The computation is made using the Huber (1967) - White (1980) sandwich estimator with Heckman (1979) correction. In the computation of leverage weights the λ's are assumed to be fixed.

Variance covariance matrix of the second stage estimator

Mikhail Zhelonkin, Marc G. Genton, Elvezio Ronchetti

Amemiya, T. (1984) Tobit Models: a Survey. Journal of Econometrics, 24, p. 3-61.

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.

ssmrob documentation built on Aug. 20, 2021, 5:08 p.m.