Two-stage residual inclusion estimation: A practitioners guide to Stata implementation
Joseph V. Terza
Department of Economics
Indiana University–Purdue University Indianapolis
Indianapolis, IN
[email protected]
|
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.
View all articles by this author:
Joseph V. Terza
View all articles with these keywords:
two-stage residual inclusion, endogeneity
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
