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Properties of Fixed Effects Dynamic Panel Data Estimators for a Typical Growth Dataset

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Abstract

This paper investigates the properties of dynamic panel data (DPD) estimators in the context of a typical growth dataset. Using Monte Carlo simulations, we compare the performance of various DPD estimators, namely the Anderson-Hsiao (AH) and Arellano-Bond's General Method of Moment (GMM) one-step and two- step estimators, using the least-square dummy variable (LSDV) as a benchmark. We arrive at three conclusions. First, LSDV produces biased estimates and the biases are significant even for a moderate-sized time dimension. Second, there is no immediately obvious choice to replace LSDV among the estimators considered here. For one, there is the bias-efficiency trade-off. In addition, differences in the characteristics of data influence the performances of the various estimators. Finally, serial correlations in the error terms, even at a low degree, can introduce significant biases to the estimations.

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Bibliographic Info

Paper provided by Centre for Strategic and International Studies, Jakarta, Indonesia in its series CSIS Economics Working Paper Series with number WPE062.

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Date of creation: May 2002
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Handle: RePEc:sis:wpecon:wpe062

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Keywords: dynamic panel data estimators; economic growth; Anderson- Hsiao (AH); General Methods of Moment (GMM); least-square dummy variable (LSDV); Monte Carlo simulation.;

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Cited by:
  1. Theo Eicher & Till Schreiber, 2010. "Institutions and Growth: Time Series Evidence from Natural Experiments," Working Papers UWEC-2007-15-P, University of Washington, Department of Economics.
  2. Stefan Sperlich & Yvonne Sperlich, 2012. "Growth and Convergence in South–South Integration Areas: Empirical Evidence," Research Papers by the Department of Economics, University of Geneva 12032, Département des Sciences Économiques, Université de Genève.

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