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Two‐Stage Residual Inclusion Estimation in Health Services Research and Health Economics

Health Services Research

Published online on


Objectives Empirical analyses in health services research and health economics often require 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, implementation of conventional regression methods that ignore endogeneity will likely produce results that are biased and not causally interpretable. Terza et al. (2008) discuss a relatively simple estimation method that avoids endogeneity bias and is applicable in a wide variety of nonlinear regression contexts. They call this method two‐stage residual inclusion (2SRI). In the present paper, I offer a 2SRI how‐to guide for practitioners and a step‐by‐step protocol that can be implemented with any of the popular statistical or econometric software packages. Study Design We introduce the protocol and its Stata implementation in the context of a real data example. Implementation of 2SRI for a very broad class of nonlinear models is then discussed. Additional examples are given. Empirical Application We analyze cigarette smoking as a determinant of infant birthweight using data from Mullahy (1997). Conclusion It is hoped that the discussion will serve as a practical guide to implementation of the 2SRI protocol for applied researchers.