Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/168360 
Year of Publication: 
2017
Series/Report no.: 
Economics Discussion Papers No. 2017-58
Publisher: 
Kiel Institute for the World Economy (IfW), Kiel
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 crosssectional 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? Applied Economics, 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.
Subjects: 
Panel data estimators
Monte Carlo simulation
PCSE
Parks model
JEL: 
C23
C33
Creative Commons License: 
cc-by Logo
Document Type: 
Working Paper

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