A Monte Carlo Evaluation of Some Common Panel Data Estimators when Serial Correlation and Cross-sectional Dependence are Both Present

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

Abstract: This study employs Monte Carlo experiments to evaluate the performances of a number of common panel data estimators when serial correlation and cross-sectional dependence are both present. It focuses on fixed effects models with less than 100 cross-sectional units and between 10 and 25 time periods (such as are commonly employed in empirical growth studies). Estimator performance is compared on two dimensions: (i) root mean square error and (ii) accuracy of estimated confidence intervals. An innovation of our study is that our simulated panel data sets are designed to look like “real-world” panel data. We find large differences in the performances of the respective estimators. Further, estimators that perform well on efficiency grounds may perform poorly when estimating confidence intervals, and vice versa. Our experimental results form the basis for a set of estimator recommendations. These are applied to “out of sample” simulated panel data sets and found to perform well.

Keywords: Panel Data estimation; Monte Carlo analysis; FGLS; PCSE; Groupwise Heteroscedasticity; Serial Correlation; Cross-sectional Dependence; Stata; EViews (search for similar items in EconPapers)
JEL-codes: C15 C23 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2007-04-30
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:07/01

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