Properties of Fixed Effects Dynamic Panel Data Estimators for a Typical Growth Dataset
AbstractThis 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 InfoPaper provided by Centre for Strategic and International Studies, Jakarta, Indonesia in its series CSIS Economics Working Paper Series with number WPE062.
Date of creation: May 2002
Date of revision:
dynamic panel data estimators; economic growth; Anderson- Hsiao (AH); General Methods of Moment (GMM); least-square dummy variable (LSDV); Monte Carlo simulation.;
Find related papers by JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
- O4 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
- O5 - Economic Development, Technological Change, and Growth - - Economywide Country Studies
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