Estimating dynamic panel data models: a practical guide for macroeconomists

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.