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A Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias

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Abstract

This study uses Monte Carlo analysis to investigate the performances of five different meta-analysis (MA) estimators: the Fixed Effects (FE) estimator, the Weighted Least Squares (WLS) estimator, the Random Effects (RE) estimator, the Precision Effect Test (PET) estimator, and the Precision Effect Estimate with Standard Errors (PEESE) estimator. We consider two types of publication bias: publication bias directed against statistically insignificant estimates, and publication bias directed against wrong-signed estimates. Finally, we consider three cases concerning the distribution of the “true effect”: the Fixed Effects case, where there is only estimate per study, and all studies have the same true effect; the Random Effects case, where there is only one estimate per study, and there is heterogeneity in true effects across studies; and the Panel Random Effects case, where studies have multiple estimates, and true effects are random both across and within studies. Our simulations produce a number of findings that challenge results from previous research.

Suggested Citation

  • W. Robert Reed & Raymond J.G.M. Florax & Jacques Poot, 2014. "A Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias," Working Papers in Economics 14/22, University of Canterbury, Department of Economics and Finance.
  • Handle: RePEc:cbt:econwp:14/22
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    File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1422.pdf
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    1. Mark Koetse & Raymond Florax & Henri Groot, 2010. "Consequences of effect size heterogeneity for meta-analysis: a Monte Carlo study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 217-236, June.
    2. Jasper M. Dalhuisen & Raymond J. G. M. Florax & JHenri L. F. de Groot & Peter Nijkamp, 2003. "Price and Income Elasticities of Residential Water Demand: A Meta-Analysis," Land Economics, University of Wisconsin Press, vol. 79(2), pages 292-308.
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    4. Hristos Doucouliagos & Janto Haman & T.D. Stanley, 2012. "Pay for Performance and Corporate Governance Reform," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 51(3), pages 670-703, July.
    5. Bellavance, Franois & Dionne, Georges & Lebeau, Martin, 2009. "The value of a statistical life: A meta-analysis with a mixed effects regression model," Journal of Health Economics, Elsevier, vol. 28(2), pages 444-464, March.
    6. Doucouliagos, Chris & Stanley, T.D. & Giles, Margaret, 2012. "Are estimates of the value of a statistical life exaggerated?," Journal of Health Economics, Elsevier, vol. 31(1), pages 197-206.
    7. Hristos Doucouliagos & Martin Paldam, 2013. "The Robust Result in Meta-analysis of Aid Effectiveness: A Response to Mekasha and Tarp," Journal of Development Studies, Taylor & Francis Journals, vol. 49(4), pages 584-587, April.
    8. Tomáš Havránek, 2015. "Measuring Intertemporal Substitution: The Importance Of Method Choices And Selective Reporting," Journal of the European Economic Association, European Economic Association, vol. 13(6), pages 1180-1204, December.
    9. Tseday Jemaneh Mekasha & Finn Tarp, 2013. "Aid and Growth: What Meta-Analysis Reveals," Journal of Development Studies, Taylor & Francis Journals, vol. 49(4), pages 564-583, April.
    10. Hristos Doucouliagos & T. D. Stanley, 2009. "Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis," British Journal of Industrial Relations, London School of Economics, vol. 47(2), pages 406-428, June.
    11. Stanley, T. D. & Jarrell, Stephen B. & Doucouliagos, Hristos, 2010. "Could It Be Better to Discard 90% of the Data? A Statistical Paradox," The American Statistician, American Statistical Association, vol. 64(1), pages 70-77.
    12. T. D. Stanley, 2008. "Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(1), pages 103-127, February.
    13. Nelson, Jon P., 2014. "Estimating the price elasticity of beer: Meta-analysis of data with heterogeneity, dependence, and publication bias," Journal of Health Economics, Elsevier, vol. 33(C), pages 180-187.
    14. Tomas Havranek & Zuzana Irsova, 2017. "Do Borders Really Slash Trade? A Meta-Analysis," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 65(2), pages 365-396, June.
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    Cited by:

    1. Martin Paldam, 2016. "Simulating an empirical paper by the rational economist," Empirical Economics, Springer, vol. 50(4), pages 1383-1407, June.
    2. Paldam, Martin, 2015. "Meta-analysis in a nutshell: Techniques and general findings," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 9, pages 1-14.
    3. Nazila Alinaghi & W. Robert Reed, 2016. "Meta-Analysis and Publication Bias: How Well Does the FAT-PET-PEESE Procedure Work?," Working Papers in Economics 16/26, University of Canterbury, Department of Economics and Finance.
    4. Gunby, Philip & Jin, Yinghua & Robert Reed, W., 2017. "Did FDI Really Cause Chinese Economic Growth? A Meta-Analysis," World Development, Elsevier, vol. 90(C), pages 242-255.
    5. Diana Zigraiova & Tomas Havranek, 2016. "Bank Competition And Financial Stability: Much Ado About Nothing?," Journal of Economic Surveys, Wiley Blackwell, vol. 30(5), pages 944-981, December.

    More about this item

    Keywords

    Meta-analysis; Random effects; Fixed effects; publication bias; Monte Carlo; Simulations;

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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