Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Terza JV

PROVIDER: S-EPMC5980262 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Terza Joseph V JV

Health services research 20170531 3

<h4>Objectives</h4>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 ...[more]

PMID: 28568477

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Dataset Information

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

Objectives

Study design

Empirical application

Conclusion

Publications

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

Similar Datasets

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