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The Robustness of Estimators for Dynamic Panel Data Models to Misspecification

Author

Listed:
  • Harris, M.N.
  • Longmire, R.J.
  • Matyas, L.

Abstract

It is well known that the usual techniques for estimating random and fixed effects panel data models are inconsistent in the dynamic setting. As a consequence, numerous consistent estimators have been proposed in the literature. However, all such estimators rely on certain well defined assumption, which in practice my be violated.The purpose of this paper is to ascertain how robust the available estimators are to such misspecifications, thus providing guidance to applied researcher as to an appropriate choice of estimator in such situation.

Suggested Citation

  • Harris, M.N. & Longmire, R.J. & Matyas, L., 1996. "The Robustness of Estimators for Dynamic Panel Data Models to Misspecification," Monash Econometrics and Business Statistics Working Papers 9/96, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:1996-9
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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, April.
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    Citations

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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

    REGRESSION ANALYSIS; ECONOMIC MODELS; EVALUATION;
    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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