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Quasi Maximum-Likelihood Estimation Of Dynamic Panel Data Models For Short Time Series

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  • Robert F. Phillips

    (The George Washington University)

Abstract

This paper establishes the almost sure convergence and asymptotic normality of quasi maximum-likelihood (QML) estimators of a dynamic panel data model when the time series for each cross section is short. The QML estimators are robust with respect to initial conditions and misspecification of the log-likelihood, and results are provided for a general specification of the error variance-covariance matrix. The paper also provides procedures for computing QML estimates that improve on computational methods previously recommended in the literature. Moreover, it compares the finite sample performance of several QML estimators, the differenced GMM estimator, and the system GMM estimator.

Suggested Citation

  • 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.
    • Handle: RePEc:gwc:wpaper:2014-006
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    File URL: https://www2.gwu.edu/~forcpgm/2014-006.pdf
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    References listed on IDEAS

    as
    1. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    2. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    4. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    5. 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.
    6. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    7. Nerlove, Marc, 1971. "Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections," Econometrica, Econometric Society, vol. 39(2), pages 359-382, March.
    8. Phillips, Robert F., 2004. "Estimation of a generalized random-effects model: some ECME algorithms and Monte Carlo evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 28(9), pages 1801-1824, July.
    9. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    10. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. 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.
    2. Cheikh Tidiane Ndiaye & Armand Akomavo Dagoudo & Babacar Mbengue, 2021. "Growth and Income Distribution Inequalities in Sub-Saharan Africa: A Dynamic Model Approach [Croissance et inégalités de distribution des revenus en Afrique subsaharienne : une approche par les mod," Working Papers hal-03202484, HAL.

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    More about this item

    Keywords

    random effects; fixed effects; differenced QML; augmented dynamic panel data model;
    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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