Abstract
Given the widespread use of panel data in dynamic models, it is worth evaluating the performance of different estimators in finite samples in the presence of low and high persistence, with the latter being present in many macroeconomic series. This article analyzes the properties of the Least Square Dummy Variable (LSDV) estimators, Arellano-Bond Generalized Method of Moments Stage 1 (AB-GMM1), BBGMM1 (), AH (Anderson-Hsiao), and Kiviet using a Monte Carlo experiment. The results show that, in the presence of low persistence, the Kiviet estimator is the best performer based on the criteria of Root-Mean-Square Error (RMSE), bias and standard deviation. Meanwhile in the case of high persistence, the system estimator of Blundell and Bond (GMM1) is the best performing estimator against their rivals, followed by Kiviet estimator that exhibits good behavior, except in micropanels.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
