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Efficient estimation of heterogeneous coefficients in panel data models with common shocks

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  • Li, Kunpeng
  • Cui, Guowei
  • Lu, Lina

Abstract

This paper investigates the estimation and inference issues of heterogeneous coefficients in panel data models with common shocks. We propose a novel two-step method to estimate the heterogeneous coefficients. We establish the asymptotic theory of our estimators, including consistency, asymptotic representation, and limiting distribution. Our two-step method can effectively address the limitations of the existing methods, such as the common correlated effects method proposed by Pesaran (2006) and the iterated principal components method proposed by Song (2013). The two-step estimator is as efficient as the two existing competitors in the basic model, and more efficient in the model with zero restrictions. Intensive Monte Carlo simulations show that the proposed estimator performs robustly in a variety of data setups.

Suggested Citation

  • Li, Kunpeng & Cui, Guowei & Lu, Lina, 2020. "Efficient estimation of heterogeneous coefficients in panel data models with common shocks," Journal of Econometrics, Elsevier, vol. 216(2), pages 327-353.
    • Handle: RePEc:eee:econom:v:216:y:2020:i:2:p:327-353
    DOI: 10.1016/j.jeconom.2019.08.011
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    More about this item

    Keywords

    Factor analysis; Block diagonal covariance; Panel data models; Common shocks; Maximum likelihood estimation; Heterogeneous coefficients; Inferential theory; Extreme value theory;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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