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

dc.contributor.authorReed, W.R.
dc.contributor.authorHaichun, Y.
dc.date.accessioned2009-05-18T20:47:02Z
dc.date.available2009-05-18T20:47:02Z
dc.date.issued2007en
dc.description.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.en
dc.identifier.citationReed, W.R., Haichun, Y. (2007) A Monte Carlo Evaluation of Some Common Panel Data Estimators when Serial Correlation and Cross-sectional Dependence are Both Present. Department of Economics. Working Paper Series..en
dc.identifier.urihttp://hdl.handle.net/10092/2463
dc.language.isoen
dc.publisherUniversity of Canterbury. Department of Economics and Financeen
dc.rights.urihttps://hdl.handle.net/10092/17651en
dc.subject.marsdenFields of Research::340000 Economics::340400 Econometrics::340405 Panel data analysisen
dc.subject.marsdenFields of Research::340000 Economics::340400 Econometrics::340402 Econometric and statistical methodsen
dc.titleA Monte Carlo Evaluation of Some Common Panel Data Estimators when Serial Correlation and Cross-sectional Dependence are Both Presenten
dc.typeDiscussion / Working Papers
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