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

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    Author
    Reed, W.R.
    Haichun, Y.
    Date
    2007
    Permanent Link
    http://hdl.handle.net/10092/2463

    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.

    Subjects
    Fields of Research::340000 Economics::340400 Econometrics::340405 Panel data analysis
     
    Fields of Research::340000 Economics::340400 Econometrics::340402 Econometric and statistical methods
    Collections
    • Business and Law: Working Papers [180]
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