Browsing by Subject "two-stage residual inclusion"

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    Two-stage residual inclusion estimation: A practitioners guide to Stata implementation
    (2017) Terza, Joseph V.; Economics, School of Liberal Arts
    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 implemented 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.