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Internal meta-analysis for Monte Carlo simulations

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Listed:
  • Andor, Mark Andreas
  • Bernstein, David H.
  • Parmeter, Christopher F.
  • Sommer, Stephan

Abstract

Monte Carlo (MC) simulations are one of the dominant approaches to compare statistical methods. To date, there is no standard procedure for MC simulations. Although internally valid, they exhibit a certain degree of arbitrariness through the various choices that researchers make. In this paper, we propose the use of an internal meta-analysis for MC simulations to allow a standardized analysis, synthesis and presentation of MC simulation results in a transparent manner. The use of an internal meta-analysis allows (i) a much more standardized procedure and (ii) comprehensive analysis of a large variety and number of simulations. To exemplify the procedure, we conduct an extensive set of simulations to compare the empirical performance of three different estimators of the generalized stochastic frontier panel data model. Besides contributing to the literature on efficiency analysis by improving the understanding of the merits of the three different estimators, we demonstrate the applicability and usefulness of internal meta-analysis for MC simulations in general.

Suggested Citation

  • Andor, Mark Andreas & Bernstein, David H. & Parmeter, Christopher F. & Sommer, Stephan, 2023. "Internal meta-analysis for Monte Carlo simulations," Ruhr Economic Papers 997, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:997
    DOI: 10.4419/9697316
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    References listed on IDEAS

    as
    1. Perelman, Sergio & Santín, Daniel, 2009. "How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement," European Journal of Operational Research, Elsevier, vol. 199(1), pages 303-310, November.
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    More about this item

    Keywords

    Monte Carlo simulation; meta-analysis; stochastic frontier analysis; productionfunction; panel data;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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