Keywords: Panel data, simulation, dynamic model, macroeconomics, growth
Abstract: We use a Monte Carlo approach to investigate the performance of several
different methods designed to reduce the bias of the estimated coefficients
for dynamic panel data models estimated with the longer, narrower panels
typical of macro data. We find that the bias of the least squares
dummy variable approach can be significant, even when the time dimension of
the panel is as large as 30. For panels with small time dimensions, we find
a corrected least squares dummy variable estimator to be the best choice.
However, as the time dimension of the panel increases, the computationally
simpler Anderson-Hsiao estimator performs equally well.
Full paper (343 KB PDF)
| Full paper (588 KB Postscript)
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Last update: July 16, 1997