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Semiparametric Estimation of Partially Linear Dynamic Panel Data Models with Fixed Effects

Author

Listed:
  • Su Liangjun

    (Singapore Management University)

  • Zhang Yonghui

    (Renmin University of China)

Abstract

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite dimensional parameter and a local polynomial estimator for the infinite dimensional parameter based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals tends to ∞ and the time period is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a nonlinear positive effect on the economic growth rate.

Suggested Citation

  • Su Liangjun & Zhang Yonghui, 2015. "Semiparametric Estimation of Partially Linear Dynamic Panel Data Models with Fixed Effects," Working Papers 06-2015, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:06-2015
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    File URL: http://ink.library.smu.edu.sg/soe_research/1712/
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    Citations

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    Cited by:

    1. Sun, Yanqing & Zhang, Yuanqing & Huang, Jianhua Z., 2019. "Estimation of a semiparametric varying-coefficient mixed regressive spatial autoregressive model," Econometrics and Statistics, Elsevier, vol. 9(C), pages 140-155.
    2. Yashar Tarverdi, 2018. "Aspects of Governance and $$\hbox {CO}_2$$ CO 2 Emissions: A Non-linear Panel Data Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(1), pages 167-194, January.
    3. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.

    More about this item

    Keywords

    Additive structure; Fredholm integral equation; Generated covariate; GMM; Local polynomial regression; Partially linear model; Sieve method; Time effect;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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