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A comparison of dynamic panel data estimators: Monte Carlo evidence and an application to the investment function

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  • Behr, Andreas

Abstract

In our analysis we discuss several dynamic panel data estimators proposed in the literature and assess their performance in Monte Carlo simulations. It is a well known fact that the natural choice, the least squares dummy variable estimator is biased in the context of dynamic estimation. The estimators taking into account the resulting bias can be grouped broadly into the class of instrumental estimators and the class of direct bias corrected estimators. The simulation results clearly favour the direct bias corrected estimators, especially the estimator proposed by Hansen (2001). The superiority of these estimators decreases with growing numbers of individuals in the simulation. This is the well known fact of large sample properties of the GMM-methods. In the case of endogenous predetermined regressors, the system-estimator proposed by Blundell and Bond is unbiased and most efficient, while direct bias corrected estimators perform similar to the GMM-estimator proposed by Arellano and Bond (1991). Turning to the empirical comparison, we find that the different estimators lead to the same conclusions concerning the investment behaviour of German manufacturing firms based on the Deutsche Bundesbank's Corporate Balance Sheet Statistics. Investment is strongly positive dependent on lagged investment and Q. Nevertheless, in detail the differences of the estimated parameters are not negligible.

Suggested Citation

  • Behr, Andreas, 2003. "A comparison of dynamic panel data estimators: Monte Carlo evidence and an application to the investment function," Discussion Paper Series 1: Economic Studies 2003,05, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdp1:4200
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    Cited by:

    1. Potrafke, Niklas, 2010. "The growth of public health expenditures in OECD countries: Do government ideology and electoral motives matter?," Journal of Health Economics, Elsevier, vol. 29(6), pages 797-810, December.
    2. Niklas Potrafke, 2013. "Economic Freedom and Government Ideology across the German States," Regional Studies, Taylor & Francis Journals, vol. 47(3), pages 433-449, March.
    3. Mario Mechtel & Niklas Potrafke, 2013. "Electoral cycles in active labor market policies," Public Choice, Springer, vol. 156(1), pages 181-194, July.
    4. Niklas Potrafke, 2011. "Does government ideology influence budget composition? Empirical evidence from OECD countries," Economics of Governance, Springer, vol. 12(2), pages 101-134, June.
    5. Lars Mewes & Tom Broekel, 2020. "Technological Complexity and Economic Growth of Regions," Papers in Evolutionary Economic Geography (PEEG) 2049, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Sep 2020.
    6. Niklas Potrafke, 2009. "Does government ideology influence political alignment with the U.S.? An empirical analysis of voting in the UN General Assembly," The Review of International Organizations, Springer, vol. 4(3), pages 245-268, September.
    7. Niklas Potrafke, 2012. "Political cycles and economic performance in OECD countries: empirical evidence from 1951–2006," Public Choice, Springer, vol. 150(1), pages 155-179, January.
    8. Niklas Potrafke, 2009. "Did globalization restrict partisan politics? An empirical evaluation of social expenditures in a panel of OECD countries," Public Choice, Springer, vol. 140(1), pages 105-124, July.
    9. He, Jie, 2006. "Pollution haven hypothesis and environmental impacts of foreign direct investment: The case of industrial emission of sulfur dioxide (SO2) in Chinese provinces," Ecological Economics, Elsevier, vol. 60(1), pages 228-245, November.
    10. Gutierrez Girault, Matias Alfredo, 2008. "Modeling extreme but plausible losses for credit risk: a stress testing framework for the Argentine Financial System," MPRA Paper 16378, University Library of Munich, Germany.
    11. Mechtel, Mario & Potrafke, Niklas, 2009. "Political Cycles in Active Labor Market Policies," MPRA Paper 14270, University Library of Munich, Germany.
    12. Niklas Potrafke, 2010. "Labor market deregulation and globalization: empirical evidence from OECD countries," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(3), pages 545-571, September.
    13. Behr Andreas, 2005. "Investment, Q and Liquidity / Investitionen, Q und Liquidität: Evidence for Germany Using Firm Level Balance Sheet Data / Empirische Ergebnisse auf Basis von Unternehmensdaten," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(1), pages 2-21, February.
    14. Petreski, Marjan, 2009. "Analysis of exchange-rate regime effect on growth: theoretical channels and empirical evidence with panel data," Economics Discussion Papers 2009-49, Kiel Institute for the World Economy (IfW).
    15. Joanna Mackiewicz­‑Łyziak, 2014. "Wpływ długu publicznego na oczekiwania inflacyjne konsumentów w Europie," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 5, pages 113-132.
    16. Beata Bal-Domańska, 2016. "The Impact of Economic Crisis on Convergence Processes in European Union Regions," Prague Economic Papers, Prague University of Economics and Business, vol. 2016(5), pages 509-526.

    More about this item

    Keywords

    dynamic panel data estimation; GMM; bias correction; investment;

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • 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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