Simpler standard errors for two-stage optimization estimators
Joseph V. Terza
Department of Economics
Indiana University Purdue University Indianapolis
Indianapolis, IN
[email protected]
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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.
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Joseph V. Terza
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two-stage optimization estimators, standard errors, asymptotic theory, endogeneity, two-stage residual inclusion, sandwich estimator
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