A Monte Carlo Evaluation of Some Common Panel Data Estimators when Serial Correlation and Cross-sectional Dependence are Both Present
AbstractThis 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.
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Bibliographic InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 07/01.
Length: 49 pages
Date of creation: 30 Apr 2007
Date of revision:
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Panel Data estimation; Monte Carlo analysis; FGLS; PCSE; Groupwise Heteroscedasticity; Serial Correlation; Cross-sectional Dependence; Stata; EViews;
Find related papers by JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-05-19 (All new papers)
- NEP-ECM-2007-05-19 (Econometrics)
- NEP-ETS-2007-05-19 (Econometric Time Series)
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