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

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posted on 2022-08-02, 11:40 authored by Yu-Chin Hsu, Jen-Che Liao, Eric S. Lin

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.

Funding

We thank the Editor Esfandiar Maasoumi and two anonymous referees for their constructive comments on earlier versions of this article. Yu-Chin Hsu gratefully acknowledges the research support from Ministry of Science and Technology of Taiwan (MOST 110-2628-H-001-007), Academia Sinica Investigator Award (AS-IA-110-H01) and Center for Research in Econometric Theory and Applications (110L9002) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education of Taiwan. Jen-Che Liao gratefully acknowledges the research support from Ministry of Science and Technology of Taiwan (MOST 102-2410-H-001-100).

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