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Two-stage residual inclusion estimation: A practitioners guide to Stata implementation

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  • Joseph V. Terza

    (Indiana University–Purdue University Indianapolis)

Abstract

Empirical econometric research often requires implementation of nonlinear models whose regressors include one or more endogenous variables— regressors that are correlated with the unobserved random component of the model. In such cases, conventional regression methods that ignore endogeneity will likely produce biased results that are not causally interpretable. Terza, Basu, and Rathouz (2008, Journal of Health Economics 27: 531–543) discuss a relatively simple estimation method (two-stage residual inclusion) that avoids endogeneity bias, is applicable in many nonlinear regression contexts, and can easily be im- plemented in Stata. In this article, I offer a step-by-step protocol to implement the two-stage residual inclusion method in Stata. I illustrate this protocol in the context of a real-data example. I also discuss other examples and pertinent Stata code. Copyright 2017 by StataCorp LP.

Suggested Citation

  • Joseph V. Terza, 2017. "Two-stage residual inclusion estimation: A practitioners guide to Stata implementation," Stata Journal, StataCorp LP, vol. 17(4), pages 916-938, December.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:916-938
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    Keywords

    two-stage residual inclusion; endogeneity;

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