Two-Stage Residual Inclusion Estimation in Health Services Research and Health Economics.

Area of Science:

• Health Services Research
• Health Economics
• Econometrics

Background:

• Empirical studies in health services research and health economics frequently encounter nonlinear models with endogenous variables.
• Ignoring endogeneity in these models leads to biased and causally uninterpretable results.
• Conventional regression methods are insufficient when regressors correlate with unobserved model components.

Purpose of the Study:

• To provide a practical, step-by-step guide for implementing the two-stage residual inclusion (2SRI) method.
• To demonstrate the application of 2SRI in addressing endogeneity bias within nonlinear regression contexts.
• To offer a protocol implementable across various statistical and econometric software packages.

Main Methods:

• The paper details the two-stage residual inclusion (2SRI) estimation method.
• A protocol is presented for practitioners, including software implementation guidance.
• The method is illustrated using a real-world data example.

Main Results:

• The 2SRI method offers a straightforward approach to mitigate endogeneity bias in nonlinear models.
• The protocol facilitates the application of 2SRI for a wide range of nonlinear models.
• The empirical example demonstrates the practical utility of 2SRI.

Conclusions:

• The discussion serves as a practical implementation guide for applied researchers utilizing the 2SRI protocol.
• 2SRI is a valuable tool for obtaining unbiased and causally interpretable results in health economics and services research.
• The presented protocol enhances the accessibility of advanced econometric techniques for empirical analysis.