Simpler standard errors for two-stage optimization estimators estimation in normal linear models

Joseph V. Terza ()
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Joseph V. Terza: Indiana University Purdue University Indianapolis

Stata Journal, 2016, vol. 16, issue 2, 368-385

Abstract: Aiming to lessen the analytic and computational burden faced by practitioners seeking to correct the standard errors of two-stage estimators, I offer a heretofore unexploited simplification of the conventional formulation for the most commonly encountered cases in empirical application—two-stage estimators that involve maximum likelihood or pseudomaximum likelihood estimation. With the applied researcher in mind, I focus on the two-stage residual inclusion estimator designed for nonlinear regression models involving endogeneity. I demonstrate the analytics and Stata and Mata code for implementing my simplified standard-error formula by applying the two-stage residual inclusion method to the birthweight model of Mullahy (1997, Review of Economics and Statistics 79: 586–593) using his original data. Copyright 2016 by StataCorp LP.

Keywords: two-stage optimization estimators; standard errors; asymptotic theory; endogeneity; two-stage residual inclusion; sandwich estimator (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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