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Robust likelihood estimation of dynamic panel data models

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  • Alvarez, Javier
  • Arellano, Manuel

Abstract

We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias-corrected conditional score estimators, random effects maximum likelihood in levels and first differences, and estimators that impose mean stationarity are considered for general autoregressive models with individual effects. We investigate identification under unit roots, and show that random effects estimation in levels may achieve substantial efficiency gains relative to estimation from data in differences. In an empirical application, we find evidence against unit roots in individual earnings processes from the Panel Study of Income Dynamics and the Spanish section of the European Community Household Panel.

Suggested Citation

  • Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
  • Handle: RePEc:eee:econom:v:226:y:2022:i:1:p:21-61
    DOI: 10.1016/j.jeconom.2021.03.005
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    More about this item

    Keywords

    Autoregressive panel data models; Time series heteroskedasticity; Bias-corrected score; Random effects; Earnings process;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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