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Do you know your biases? A Monte Carlo analysis of dynamic panel data estimators

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  • Kufenko, Vadim
  • Prettner, Klaus

Abstract

We assess the performance of widely-used dynamic panel data estimators based on Monte Carlo simulations of a dynamic economic process. Knowing the true underlying coefficient of the autoregressive term, we show that most estimators exhibit a severe bias even in the absence of measurement errors, omitted variables, and endogeneity issues. We analyze how the bias changes with the sample size, the autoregressive coefficient, and the estimation options. Based on our insights, we recommend i) carefully choosing appropriate estimators given the underlying structure of the data and ii) scrutinizing the estimation results based on the insights of simulation studies.

Suggested Citation

  • Kufenko, Vadim & Prettner, Klaus, 2021. "Do you know your biases? A Monte Carlo analysis of dynamic panel data estimators," Department of Economics Working Paper Series 316, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus005:8285
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    1. Jakob B. Madsen & Md. Rabiul Islam & James B. Ang, 2010. "Catching up to the technology frontier: the dichotomy between innovation and imitation," Canadian Journal of Economics, Canadian Economics Association, vol. 43(4), pages 1389-1411, November.
    2. Sèna Kimm Gnangnon & Jean‐François Brun, 2019. "Trade openness, tax reform and tax revenue in developing countries," The World Economy, Wiley Blackwell, vol. 42(12), pages 3515-3536, December.
    3. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    4. Crespo Cuaresma, Jesus & Lábaj, Martin & Pružinský, Patrik, 2014. "Prospective ageing and economic growth in Europe," The Journal of the Economics of Ageing, Elsevier, vol. 3(C), pages 50-57.
    5. Bun, Maurice J. G. & Kiviet, Jan F., 2003. "On the diminishing returns of higher-order terms in asymptotic expansions of bias," Economics Letters, Elsevier, vol. 79(2), pages 145-152, May.
    6. Stephan Litschig & María Lombardi, 2019. "Which tail matters? Inequality and growth in Brazil," Journal of Economic Growth, Springer, vol. 24(2), pages 155-187, June.
    7. Paul Johnson & Chris Papageorgiou, 2020. "What Remains of Cross-Country Convergence?," Journal of Economic Literature, American Economic Association, vol. 58(1), pages 129-175, March.
    8. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    9. Michał Jerzmanowski, 2017. "Finance and sources of growth: evidence from the U.S. states," Journal of Economic Growth, Springer, vol. 22(1), pages 97-122, March.
    10. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    11. de la Fuente, Angel, 1997. "The empirics of growth and convergence: A selective review," Journal of Economic Dynamics and Control, Elsevier, vol. 21(1), pages 23-73, January.
    12. William Hauk & Romain Wacziarg, 2009. "A Monte Carlo study of growth regressions," Journal of Economic Growth, Springer, vol. 14(2), pages 103-147, June.
    13. Sebastian Kripfganz, 2019. "Generalized method of moments estimation of linear dynamic panel-data models," London Stata Conference 2019 17, Stata Users Group.
    14. Sebastian Kripfganz, 2016. "Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models," Stata Journal, StataCorp LP, vol. 16(4), pages 1013-1038, December.
    15. McArthur, John W. & McCord, Gordon C., 2017. "Fertilizing growth: Agricultural inputs and their effects in economic development," Journal of Development Economics, Elsevier, vol. 127(C), pages 133-152.
    16. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    17. Caselli, Francesco & Esquivel, Gerardo & Lefort, Fernando, 1996. "Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics," Journal of Economic Growth, Springer, vol. 1(3), pages 363-389, September.
    18. Sebastian Kripfganz, 2016. "XTDPDQML: Stata module to perform quasi-maximum likelihood linear dynamic panel data estimation," Statistical Software Components S458218, Boston College Department of Economics, revised 04 Mar 2017.
    19. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    20. Maria Abreu & Henri L. F. de Groot & Raymond J. G. M. Florax, 2005. "A Meta‐Analysis of β‐Convergence: the Legendary 2%," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 389-420, July.
    21. Hauk, William R., 2017. "Endogeneity bias and growth regressions," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 143-161.
    22. Bruno, Giovanni S.F., 2005. "Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models," Economics Letters, Elsevier, vol. 87(3), pages 361-366, June.
    23. 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.
    24. Pedro Cavalcanti Ferreira & JosÈ Luiz Rossi, 2003. "New Evidence from Brazil on Trade Liberalization and Productivity Growth," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(4), pages 1383-1405, November.
    25. Philippe Aghion & Steven Durlauf (ed.), 2005. "Handbook of Economic Growth," Handbook of Economic Growth, Elsevier, edition 1, volume 1, number 1.
    26. Pesaran, M. Hashem, 2015. "Time Series and Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780198759980.
    27. Jones, Charles I, 1995. "R&D-Based Models of Economic Growth," Journal of Political Economy, University of Chicago Press, vol. 103(4), pages 759-784, August.
    28. Gnangnon, Sena Kimm, 2019. "Tax Reform and Trade Openness in Developing Countries," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 34(3), pages 498-519.
    29. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    30. Crepon, Bruno & Duguet, Emmanuel, 1997. "Estimating the Innovation Function from Patent Numbers: GMM on Count Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 243-263, May-June.
    31. 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.
    32. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    33. Nazrul Islam, 1995. "Growth Empirics: A Panel Data Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(4), pages 1127-1170.
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    More about this item

    Keywords

    Theory-Based Monte Carlo Simulation; Dynamic Panel Data Estimators; Estimator Bias; Robustness of Empirical Results;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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