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Two-step series estimation and specification testing of (partially) linear models with generated regressors

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  • Yu-Chin Hsu
  • Jen-Che Liao
  • Eric S. Lin

Abstract

This paper studies three semiparametric models that are useful and frequently encountered in applied econometric work—a linear and two partially linear specifications with generated regressors, i.e., the regressors that are unobserved, but can be nonparametrically estimated from the data. Our framework allows for generated regressors to appear in linear or nonlinear components of partially linear models. We propose two-step series estimators for the finite-dimensional parameters, establish their n-consistency (with sample size n) and asymptotic normality, and provide the asymptotic variance formulae that take into account the estimation error of generated regressors. Moreover, we develop a nonparametric specification test for the models considered. Numerical performances of the proposed estimators and test via simulation experiments and an empirical application illustrate the utility of our approach.

Suggested Citation

  • Yu-Chin Hsu & Jen-Che Liao & Eric S. Lin, 2022. "Two-step series estimation and specification testing of (partially) linear models with generated regressors," Econometric Reviews, Taylor & Francis Journals, vol. 41(9), pages 985-1007, September.
    • Handle: RePEc:taf:emetrv:v:41:y:2022:i:9:p:985-1007
    DOI: 10.1080/07474938.2022.2082169
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