Abstract
Abstract This study uses Monte Carlo experiments to produce new evidence on the performance of a wide range of panel data estimators. It focuses on estimators that are readily available in statistical software packages such as Stata and Eviews, and for which the number of cross-sectional units (N) and time periods (T) are small to moderate in size. The goal is to develop practical guidelines that will enable researchers to select the best estimator for a given type of data. It extends a previous study on the subject (Reed and Ye, Which panel data estimator should I use? 2011), and modifies their recommendations. The new recommendations provide a (virtually) complete decision tree: When it comes to choosing an estimator for efficiency, it uses the size of the panel dataset (N and T) to guide the researcher to the best estimator. When it comes to choosing an estimator for hypothesis testing, it identifies one estimator as superior across all the data scenarios included in the study. An unusual finding is that researchers should use different estimators for estimating coefficients and testing hypotheses. The authors present evidence that bootstrapping allows one to use the same estimator for both.
Highlights
For applied researchers using panel data, there is an abundance of possible estimators one can choose
The Panel-Corrected Standard Errors (PCSE) estimator has proven very popular, as evidenced by over 2000 citations in Web of Science. All of this has opened up a myriad of choices for applied researchers when it comes to choosing a panel data estimator. It is in this context that Reed and Ye (2011), RY, conducted Monte Carlo experiments to test a large number of OLS and Feasible Generalized Least Squares (FGLS)-type panel data estimators, including the estimators studied by BK
They studied panel datasets for which the number of cross-sectional units (N) and time periods (T) were small to moderate in size
Summary
For applied researchers using panel data, there is an abundance of possible estimators one can choose. In its general form, with groupwise heteroskedasticity, group-wise specific AR(1) autocorrelation, and time-invariant cross-sectional correlation, the classic Parks model has a total of NN2+23NN unique parameters in the error variance-covariance matrix (EVCM), where N is the number of cross-sectional units. It is in this context that Reed and Ye (2011), RY, conducted Monte Carlo experiments to test a large number of OLS and FGLS-type panel data estimators, including the estimators studied by BK They studied panel datasets for which the number of cross-sectional units (N) and time periods (T) were small to moderate in size.
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