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Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators

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  • Okui, Ryo

Abstract

Testing the presence of serial correlation in the error terms in fixed effects regression models is important for many reasons. This paper proposes portmanteau tests based on the sum of the squares of autocorrelation estimators. This approach is a direct extension of the Box–Pierce or Ljung–Box test from single time series to panel data settings. In fixed effects regression analysis, we may estimate the autocorrelations using the within-group autocorrelations of the residuals. However, the within-group autocorrelations may be severely biased when the length of the time series is not very large compared with the cross-sectional sample size, as a result of the incidental parameters problem. We overcome this problem by using asymptotically unbiased autocorrelation estimators for long panel data recently proposed by the author. Monte Carlo simulations reveal that the proposed tests have good size properties and are powerful against a wide range of alternatives.

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  • Okui, Ryo, 2009. "Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2897-2909.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:9:p:2897-2909
    DOI: 10.1016/j.matcom.2008.08.006
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    Cited by:

    1. Jochmans, Koen, 2020. "A Portmanteau Test For Correlation In Short Panels," Econometric Theory, Cambridge University Press, vol. 36(6), pages 1159-1166, December.
    2. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    3. Okui Ryo, 2014. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 1-53, July.
    4. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.

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