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A Monte Carlo analysis of alternative meta-analysis estimators in the presence of publication bias

Listed author(s):
  • Reed, W. Robert

A meta-analysis (MA) aggregates estimated effects from many studies to calculate a single, overall effect. There is no one, generally accepted procedure for how to do this. Several estimators are commonly used, though little is known about their relative performance. A complication arises when the sample of published studies is subject to sample selection due to "publication bias." This study uses Monte Carlo simulations to investigate the performance of five different MA estimators in the presence of publication bias. The author considers two kinds of publication bias: publication bias directed against statistically insignificant estimates, and publication bias directed against wrong-signed estimates. The experiments simulate two data environments. In the Random Effects environment, each study produces only one estimate and the true effect differs across studies. In the Panel Random Effects environment, each study produces multiple estimates, and the true effect differs both within and across studies. The simulations produce a number of findings that challenge results from previous research.

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File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2015-30
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File URL: https://www.econstor.eu/bitstream/10419/120919/1/836119290.pdf
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Article provided by Kiel Institute for the World Economy (IfW) in its journal Economics: The Open-Access, Open-Assessment E-Journal.

Volume (Year): 9 (2015)
Issue (Month): ()
Pages: 1-40

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Handle: RePEc:zbw:ifweej:201530
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  1. T.D. Stanley, 2006. "Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection," Economics Series 2006_20, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  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.
  3. Hristos Doucouliagos & T.D. Stanley & Margaret Giles, 2011. "Are Estimates of the Value of a Statistical Life Exaggerated?," Economics Series 2011_2, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  4. Tseday Jemaneh Mekasha & Finn Tarp, 2011. "Aid and Growth What Meta-Analysis Reveals," WIDER Working Paper Series 022, World Institute for Development Economic Research (UNU-WIDER).
  5. Hristos Doucouliagos & T.D. Stanley, 2008. "Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis," Economics Series 2008_14, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  6. 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.
  7. 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.
  8. Hristos Doucouliagos & Martin Paldam, 2012. "The robust result in meta-analysis of aid effectiveness: A response to Mekasha and Tarp," Economics Series 2012_4, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
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  1. Meta-Analysis in Economics

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