etreg2steprobVcov: Variance Covariance Matrix

In ssmrob: Robust Estimation and Inference in Sample Selection Models

Description

Computation of the asymptotic variance matrix of the robust Heckman's two-stage estimator for endogenous treatment model.

Usage

etreg2steprobVcov(y1vec, y2vec, x1Matr, x2Matr, eststage1, eststage2, eststage2sigma, 
				  weights = rep(1, nrow(y1vec)), t.c = 1.345)

Arguments

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	robustness weights
t.c	tuning constant of the second stage

Details

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

Value

Variance covariance matrix of the second stage estimator

Author(s)

Mikhail Zhelonkin

References

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.