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Semiparametric Estimation of Partially Varying-Coefficient Dynamic Panel Data Models

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  • Zongwu Cai
  • Linna Chen
  • Ying Fang

Abstract

This paper studies a new class of semiparametric dynamic panel data models, in which some of coefficients are allowed to depend on other informative variables and some of the regressors can be endogenous. To estimate both parametric and nonparametric coefficients, a three-stage estimation method is proposed. A nonparametric generalized method of moments (GMM) is adopted to estimate all coefficients firstly and an average method is used to obtain the root-N consistent estimator of parametric coefficients. At the last stage, the estimator of varying coefficients is obtained by the partial residuals. The consistency and asymptotic normality of both estimators are derived. Monte Carlo simulations are conducted to verify the theoretical results and to demonstrate that the proposed estimators perform well in a finite sample.

Suggested Citation

  • Zongwu Cai & Linna Chen & Ying Fang, 2015. "Semiparametric Estimation of Partially Varying-Coefficient Dynamic Panel Data Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 695-719, December.
    • Handle: RePEc:taf:emetrv:v:34:y:2015:i:6-10:p:695-719
    DOI: 10.1080/07474938.2014.956569
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    4. Changjun Jiang & Jintao Li, 2022. "Influence of the Market Supply of Construction Land on the Misallocation of Labor Resources: Empirical Evidence from China," Land, MDPI, vol. 11(10), pages 1-18, October.
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