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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
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    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. -- In der vorliegenden Arbeit werden verschiedene in der Literatur vorgeschlagen dynamische Schätzer für Paneldaten diskutiert und im Rahmen einer Monte Carlo-Studie verglichen. Es ist wohlbekannt, dass der Least Squares Dummy Variable-Estimator für den Fall verzögerter endogener erklärender Variablen einen Bias aufweist. Die diskutierten Schätzer lassen sich zwei unterschiedlichen Klassen zuordnen, einer Klasse von Instrumentenschätzern und einer Klasse von biaskorrigierten Schätzern. Den Ergebnissen der Simulationsstudie zufolge sind die biaskorrigierten Schätzer leicht überlegen, insbesondere die von Hansen (2001) vorgeschlagene Biaskorrektur. Die Überlegenheit nimmt jedoch mit wachsender Zahl der beobachteten Einheiten ab. Hier spiegeln sich die bekannt günstigen Eigenschaften von GMM-Schätzern bei großer Beobachtungszahl wider. Im Falle endogener vorherbestimmter Regressoren weist der von Blundell und Bond (1998) vorgeschlagene System-GMM-Schätzer die höchste Effizienz auf. Biaskorrigierte Schätzer führen hier zu vergleichbaren Ergebnissen wie der GMMSchätzer von Arellano und Bond (1991). Bei der empirischen Anwendung zur Schätzung von dynamischen Q-Invstitionsfunktionen für Unternehmen des deutschen Verarbeitenden Gewerbes auf Grundlage der Bilanzstatistik der Deutschen Bundesbank, zeigt sich eine starke positive Abhängigkeit der Investitionen, sowohl von den Vorjahresinvestitionen als auch von Q. Bei gleicher ökonomischer Grundaussage weisen die mittels der verschiedenen diskutierten Methoden geschätzten Parameter jedoch nicht zu vernachlässigende Unterschiede auf.

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    Bibliographic Info

    Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2003,05.

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    Date of creation: 2003
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    Handle: RePEc:zbw:bubdp1:4200

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

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