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Combined Estimation of Semiparametric Panel Data Models

Author

Listed:
  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

  • Bai Huang

    (Central University of Finance and Economics)

  • Aman Ullah

    (University of California Riverside)

Abstract

The combined estimation for the semiparametric panel data models is proposed. The properties of estimators for the semiparametric panel data models with random effects (RE) and fixed effects (FE) are examined. When the RE estimator suffers from endogeneity due to the individual effects correlated with the regressors, the semiparametric RE and FE estimators may be adaptively combined, with the combining weights depending on the degree of endogeneity. The asymptotic distributions of these three estimators (RE, FE, and combined estimators) for the semiparametric panel data models are derived using a local asymptotic framework. These three estimators are then compared in asymptotic risk. The semiparametric combined estimator has strictly smaller asymptotic risk than the semiparametric fixed effect estimator. The Monte Carlo study shows that the semiparametric combined estimator outperforms semiparametric FE and RE estimators except when the degrees of endogeneity and heterogeneity of the individual effects are very small. Also presented is an empirical application where the effect of public sector capital in the private economy production function is examined using the US state level panel data.

Suggested Citation

  • Tae-Hwy Lee & Bai Huang & Aman Ullah, 2018. "Combined Estimation of Semiparametric Panel Data Models," Working Papers 201915, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:201915
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    2. Muhammad Qasim, 2024. "A weighted average limited information maximum likelihood estimator," Statistical Papers, Springer, vol. 65(5), pages 2641-2666, July.
    3. Bagstad, Kenneth J. & Ingram, Jane Carter & Shapiro, Carl D. & La Notte, Alessandra & Maes, Joachim & Vallecillo, Sara & Casey, C. Frank & Glynn, Pierre D. & Heris, Mehdi P. & Johnson, Justin A. & Lau, 2021. "Lessons learned from development of natural capital accounts in the United States and European Union," Ecosystem Services, Elsevier, vol. 52(C).
    4. Hensher, David A., 2021. "The case for negotiated contracts under the transition to a green bus fleet," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 255-269.
    5. Gupta, Joyeeta & Bavinck, Maarten & Ros-Tonen, Mirjam & Asubonteng, Kwabena & Bosch, Hilmer & van Ewijk, Edith & Hordijk, Michaela & Van Leynseele, Yves & Lopes Cardozo, Mieke & Miedema, Esther & Pouw, 2021. "COVID-19, poverty and inclusive development," World Development, Elsevier, vol. 145(C).
    6. Günther, Jutta (Ed.) & Wedemeier, Jan (Ed.), 2020. "Struktureller Umbruch durch COVID-19: Implikationen für die Innovationspolitik im Land Bremen," HWWI Policy Papers 128, Hamburg Institute of International Economics (HWWI).

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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