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The Robustness Of Estimators For Dynamic Panel Data Models To Misspecification

Author

Listed:
  • MARK N. HARRIS

    (Department of Econometrics and Business Statistics, Monash University, Clayton, Melbourne, Victoria 3800, Australia)

  • WEIPING KOSTENKO

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, Australia)

  • LÁSZLÓ MÁTYÁS

    (Central European University, Hungary;
    Erudite, Universite Paris XII, Paris, France)

  • ISFAAQ TIMOL

    (Department of Econometrics and Business Statistics, Monash University, Clayton, Melbourne, Victoria 3800, Australia)

Abstract
Transition from economic theory to a testable form of model invariably involves the use of certain "simplifying assumptions." If, however, these are not valid, misspecified models result. This article considers estimation of the dynamic linear panel data model, which often forms the basis of testable economic hypotheses. The estimators of such a model are frequently similarly based on certain assumptions which appear to be often untenable in practice. Here, the performance of these estimators is analyzed in scenarios where the theoretically required conditions are not met. Specifically, we consider three such instances of serial correlation of the idiosyncratic disturbance terms; correlation of the idiosyncratic disturbance terms and explanatory variables; and, finally, cross-sectional dependence (as a robustness check to these findings, we also consider correlations between observed and unobserved heterogeneity terms). The major findings are that the limited tests readily available tend to have poor power properties and that estimators' performance varies greatly across scenarios. In such a wide array of experiments, it is difficult to pick-out just one "winner." However, a robust estimator across all experiments and parameter settings was a variant of the Wansbeek–Bekker estimator. This is a significant finding, as this estimator is infrequently used in practice. When the experiments are extended to include correlations between observed and unobserved heterogeneity terms, one might also consider, for across-the-board performance, the Blundell and Bond estimator.

Suggested Citation

  • Mark N. Harris & Weiping Kostenko & László Mátyás & Isfaaq Timol, 2009. "The Robustness Of Estimators For Dynamic Panel Data Models To Misspecification," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 54(03), pages 399-426.
    • Handle: RePEc:wsi:serxxx:v:54:y:2009:i:03:n:s0217590809003409
    DOI: 10.1142/S0217590809003409
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    References listed on IDEAS

    as
    1. Ruth A. Judson & Ann L. Owen, "undated". "Estimating Dynamic Panel Data Models: A Practical Guide for Macroeconomists," Finance and Economics Discussion Series 1997-03, Board of Governors of the Federal Reserve System (U.S.), revised 10 Dec 2019.
    2. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Aninday Banerjee & Markus Eberhardt & J James Reade, 2010. "Panel Estimation for Worriers," Discussion Papers 10-33, Department of Economics, University of Birmingham.
    2. Simon Feeny & Mark Harris & Mark Rogers, 2005. "A dynamic panel analysis of the profitability of Australian tax entities," Empirical Economics, Springer, vol. 30(1), pages 209-233, January.
    3. Jan Kiviet & Milan Pleus & Rutger Poldermans, 2017. "Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models," Econometrics, MDPI, vol. 5(1), pages 1-54, March.
    4. Magerman, Glenn & Studnicka, Zuzanna & Van Hove, Jan, 2016. "Distance and border effects in international trade: A comparison of estimation methods," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-31.
    5. Mark N. Harris & Simon Feeny, 2000. "Habit Persistence in Effective Tax Rates: Evidence Using Australian Tax Entities," Melbourne Institute Working Paper Series wp2000n13, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    6. Michael Lee & Ritchard Longmire & Laszlo Matyas & Mark Harris, 1998. "Growth convergence: some panel data evidence," Applied Economics, Taylor & Francis Journals, vol. 30(7), pages 907-912.
    7. Jan F. Kiviet & Milan Pleus & Rutger Poldermans, 2014. "Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models," UvA-Econometrics Working Papers 14-09, Universiteit van Amsterdam, Dept. of Econometrics.
    8. Zheka Vitaliy, 2010. "The impact of corporate governance practices on dynamic adjustment of capital structure of companies in Ukraine," EERC Working Paper Series 10/07e, EERC Research Network, Russia and CIS.
    9. Durand, Robert B. & Greene, William H. & Harris, Mark N. & Khoo, Joye, 2022. "Heterogeneity in speed of adjustment using finite mixture models," Economic Modelling, Elsevier, vol. 107(C).
    10. Mark Harris & Simon Feeny, 2003. "Habit persistence in effective tax rates," Applied Economics, Taylor & Francis Journals, vol. 35(8), pages 951-958.

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    More about this item

    Keywords

    Dynamic panel data; misspecification; IV/GMM estimation; C13; C15; C23;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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