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Estimation and Inference for Linear Panel Data Models Under Misspecification When Both n and T are Large


  • Antonio F. Galvao
  • Kengo Kato


This article considers fixed effects (FE) estimation for linear panel data models under possible model misspecification when both the number of individuals, n , and the number of time periods, T , are large. We first clarify the probability limit of the FE estimator and argue that this probability limit can be regarded as a pseudo-true parameter. We then establish the asymptotic distributional properties of the FE estimator around the pseudo-true parameter when n and T jointly go to infinity. Notably, we show that the FE estimator suffers from the incidental parameters bias of which the top order is O ( T -super- - 1), and even after the incidental parameters bias is completely removed, the rate of convergence of the FE estimator depends on the degree of model misspecification and is either ( nT )-super- - 1/2 or n -super- - 1/2. Second, we establish asymptotically valid inference on the (pseudo-true) parameter. Specifically, we derive the asymptotic properties of the clustered covariance matrix (CCM) estimator and the cross-section bootstrap, and show that they are robust to model misspecification. This establishes a rigorous theoretical ground for the use of the CCM estimator and the cross-section bootstrap when model misspecification and the incidental parameters bias (in the coefficient estimate) are present. We conduct Monte Carlo simulations to evaluate the finite sample performance of the estimators and inference methods, together with a simple application to the unemployment dynamics in the U.S.

Suggested Citation

  • Antonio F. Galvao & Kengo Kato, 2014. "Estimation and Inference for Linear Panel Data Models Under Misspecification When Both n and T are Large," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 285-309, April.
  • Handle: RePEc:taf:jnlbes:v:32:y:2014:i:2:p:285-309
    DOI: 10.1080/07350015.2013.875473

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    Cited by:

    1. Fernández-Val, Iván & Weidner, Martin, 2016. "Individual and time effects in nonlinear panel models with large N, T," Journal of Econometrics, Elsevier, vol. 192(1), pages 291-312.
    2. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825,, revised Mar 2018.
    3. Ryo Okui & Takahide Yanagi, 2014. "Panel Data Analysis with Heterogeneous Dynamics," KIER Working Papers 906, Kyoto University, Institute of Economic Research.
    4. repec:gam:jecnmx:v:4:y:2015:i:1:p:2:d:61252 is not listed on IDEAS
    5. Ying-Ying Lee, 2015. "Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-14, December.
    6. repec:bla:jecrev:v:68:y:2017:i:3:p:283-304 is not listed on IDEAS

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