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More Evidence On “Which Panel Data Estimator Should I Use?”

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Abstract

This study uses Monte Carlo experiments to produce new evidence on the performance of a wide range of panel data estimators. It focuses on estimators that are readily available in statistical software packages such as Stata and Eviews, and for which the number of cross-sectional units (N) and time periods (T) are small to moderate in size. The goal is to develop practical guidelines that will enable researchers to select the best estimator for a given type of data. It extends a previous study on the subject (Reed and Ye, 2011), and modifies their recommendations. The new recommendations provide a (virtually) complete decision tree: When it comes to choosing an estimator for efficiency, it uses the size of the panel dataset (N and T) to guide the researcher to the best estimator. When it comes to choosing an estimator for hypothesis testing, it identifies one estimator as superior across all the data scenarios included in the study. An unusual finding is that researchers should use different estimators for estimating coefficients and testing hypotheses. We present evidence that bootstrapping allows one to use the same estimator for both.

Suggested Citation

  • Mantobaye Moundigbaye & William Rea & W. Robert Reed, 2016. "More Evidence On “Which Panel Data Estimator Should I Use?”," Working Papers in Economics 16/18, University of Canterbury, Department of Economics and Finance.
    • Handle: RePEc:cbt:econwp:16/18
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    More about this item

    Keywords

    Panel Data Estimators; Monte Carlo simulation; PCSE; Parks model; Bootstrapping;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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