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A Practitioners Guide to Implementing the Two-Stage Residual Inclusion Method in Stata


  • Joseph Terza

    (Department of Economics, Indiana University Purdue University Indianapolis)


Empirical analyses 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. Failure to account for such correlation in estimation leads to bias and produces results that are not causally interpretable. Terza et al. (2008) discuss a relatively simple estimation method that avoids endogeneity bias and is applicable in wide variety of nonlinear regression contexts – two-stage residual inclusion (2SRI). We offer a 2SRI how-to guide for practitioners, and demonstrate how the method can be easily implemented in Stata, complete with correct asymptotic standard errors for the parameter estimates [see Terza (2015)]. We illustrate our suggested step-by-step protocol in the context of a real data example with Stata code. Other examples are discussed; also coded in Stata.

Suggested Citation

  • Joseph Terza, 2016. "A Practitioners Guide to Implementing the Two-Stage Residual Inclusion Method in Stata," 2016 Stata Conference 28, Stata Users Group.
  • Handle: RePEc:boc:scon16:28

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    2. Manuel Serrano-Alarcón & Helena Hernández-Pizarro & Guillem López i Casasnovas & Catia Nicodemo, 2021. "The effect of Long-Term Care (LTC) benefits on healthcare use," Working Papers 2021-12, FEDEA.

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