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Quasi maximum likelihood estimation of dynamic panel data models

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  • Robert F. Phillips

Abstract

This article establishes the almost sure convergence and asymptotic normality of levels and differenced quasi maximum likelihood (QML) estimators of dynamic panel data models. The QML estimators are robust with respect to initial conditions, conditional and time-series heteroskedasticity, and misspecification of the log-likelihood. The article also provides an ECME algorithm for calculating levels QML estimates. Finally, it compares the finite-sample performance of levels and differenced QML estimators, the differenced generalized method of moments (GMM) estimator, and the system GMM estimator. The QML estimators usually have smaller— typically substantially smaller—bias and root mean squared errors than the panel data GMM estimators.

Suggested Citation

  • Robert F. Phillips, 2018. "Quasi maximum likelihood estimation of dynamic panel data models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(16), pages 3970-3986, August.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:16:p:3970-3986
    DOI: 10.1080/03610926.2017.1366521
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    1. Robert F. Phillips, 2014. "Quasi Maximum-Likelihood Estimation Of Dynamic Panel Data Models For Short Time Series," Working Papers 2014-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Phillips, Robert F., 2010. "Iterated Feasible Generalized Least-Squares Estimation of Augmented Dynamic Panel Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 410-422.
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    Cited by:

    1. Helmers, Viola & van der Werf, Edwin, 2022. "Did the German Aviation Tax Affect Passenger Numbers? New Evidence Employing Difference-in-differences," VfS Annual Conference 2022 (Basel): Big Data in Economics 264118, Verein für Socialpolitik / German Economic Association.
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    3. Jifeng Mu & Ellen Thomas & Jiayin Qi & Yong Tan, 2018. "Online group influence and digital product consumption," Journal of the Academy of Marketing Science, Springer, vol. 46(5), pages 921-947, September.
    4. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.

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    More about this item

    JEL classification:

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

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