Two‐Stage Residual Inclusion Estimation in Health Services Research and Health Economics
本文为应用研究者提供两阶段残差包含（2SRI）方法的操作指南，通过真实数据示例和Stata实现步骤，帮助解决非线性模型中内生变量导致的估计偏误问题。
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.