Department of Economics, Indiana University Purdue University Indianapolis, Indianapolis, IN.
Health Serv Res. 2018 Jun;53(3):1890-1899. doi: 10.1111/1475-6773.12714. Epub 2017 May 31.
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.
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.
We analyze cigarette smoking as a determinant of infant birthweight using data from Mullahy (1997).
It is hoped that the discussion will serve as a practical guide to implementation of the 2SRI protocol for applied researchers.
在卫生服务研究和卫生经济学的实证分析中，经常需要实施非线性模型，其回归变量包括一个或多个内生变量——与模型中未观测到的随机分量相关的回归变量。在这种情况下，实施忽略内生性的传统回归方法可能会产生有偏差且不能进行因果解释的结果。Terza 等人（2008 年）讨论了一种相对简单的估计方法，该方法避免了内生性偏差，适用于各种非线性回归情况。他们称这种方法为两阶段残差纳入（2SRI）。在本文中，我为从业者提供了一个 2SRI 的操作指南，以及一个可以使用任何流行的统计或计量经济学软件包实现的分步协议。
我们在一个真实数据示例的背景下介绍了该协议及其 Stata 实现。然后讨论了对非常广泛的非线性模型实施 2SRI 的问题。还给出了其他示例。
我们使用 Mullahy（1997 年）的数据，分析了吸烟作为婴儿出生体重决定因素的情况。
希望本文的讨论可以为应用研究人员实施 2SRI 协议提供实用指南。
