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Identifying Genuine Effects in Observational Research by Means of Meta-Regressions

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  • Stephan B. Bruns

    (Max Planck Institute of Economics, Jena)

Abstract

Meta-regression models are increasingly utilized to integrate empirical results across studies while controlling for the potential threats of data-mining and publication bias. We propose extended meta-regression models and evaluate their performance in identifying genuine em- pirical effects by means of a comprehensive simulation study for various scenarios that are prevalent in empirical economics. We can show that the meta-regression models here pro- posed systematically outperform the prior gold standard of meta-regression analysis of re- gression coefficients. Most meta-regression models are robust to the presence of publication bias, but data-mining bias leads to seriously inflated type I errors and has to be addressed explicitly.

Suggested Citation

  • Stephan B. Bruns, 2013. "Identifying Genuine Effects in Observational Research by Means of Meta-Regressions," Jena Economics Research Papers 2013-040, Friedrich-Schiller-University Jena.
    • Handle: RePEc:jrp:jrpwrp:2013-040
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    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    2. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
    3. T. D. Stanley & Stephen B. Jarrell, 2005. "Meta‐Regression Analysis: A Quantitative Method of Literature Surveys," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 299-308, July.
    4. James H. Stock, 2010. "The Other Transformation in Econometric Practice: Robust Tools for Inference," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 83-94, Spring.
    5. Card, David & Krueger, Alan B, 1995. "Time-Series Minimum-Wage Studies: A Meta-analysis," American Economic Review, American Economic Association, vol. 85(2), pages 238-243, May.
    6. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    7. Hristos Doucouliagos & Martin Paldam, 2005. "Aid Effectiveness on Growth. A Meta Study," Economics Working Papers 2005-13, Department of Economics and Business Economics, Aarhus University.
    8. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
    9. 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.
    10. David Card & Stefano DellaVigna, 2013. "Nine Facts about Top Journals in Economics," Journal of Economic Literature, American Economic Association, vol. 51(1), pages 144-161, March.
    11. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    12. Leamer, Edward E & Leonard, Herman B, 1983. "Reporting the Fragility of Regression Estimates," The Review of Economics and Statistics, MIT Press, vol. 65(2), pages 306-317, May.
    13. Frey, Bruno S, 2003. "Publishing as Prostitution?--Choosing between One's Own Ideas and Academic Success," Public Choice, Springer, vol. 116(1-2), pages 205-223, July.
    14. T. D. Stanley, 2005. "Integrating the Empirical Tests of the Natural Rate Hypothesis: A Meta‐Regression Analysis," Kyklos, Wiley Blackwell, vol. 58(4), pages 611-634, November.
    15. Stephan B. Bruns, Christian Gross and David I. Stern, 2014. "Is There Really Granger Causality Between Energy Use and Output?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    16. Stanley, T. D. & Doucouliagos, Chris, 2007. "Identifying and correcting publication selection bias in the efficiency-wage literature: Heckman Meta-Regression," Working Papers eco_2007_11, Deakin University, Department of Economics.
    17. 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.
    18. 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.
    19. Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
    20. Sims, Christopher A, 1988. "Uncertainty across Models," American Economic Review, American Economic Association, vol. 78(2), pages 163-167, May.
    21. Neal S Young, 2008. "Why Current Publication May Distort Science," Working Papers id:1757, eSocialSciences.
    22. T. D. Stanley, 2005. "Beyond Publication Bias," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 309-345, July.
    23. Neal S Young & John P A Ioannidis & Omar Al-Ubaydli, 2008. "Why Current Publication Practices May Distort Science," PLOS Medicine, Public Library of Science, vol. 5(10), pages 1-5, October.
    24. T. D. Stanley, 2001. "Wheat from Chaff: Meta-analysis as Quantitative Literature Review," Journal of Economic Perspectives, American Economic Association, vol. 15(3), pages 131-150, Summer.
    25. Rosenbusch, Nina & Brinckmann, Jan & Müller, Verena, 2013. "Does acquiring venture capital pay off for the funded firms? A meta-analysis on the relationship between venture capital investment and funded firm financial performance," Journal of Business Venturing, Elsevier, vol. 28(3), pages 335-353.
    26. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
    27. Chris Doucouliagos & T.D. Stanley, 2013. "Are All Economic Facts Greatly Exaggerated? Theory Competition And Selectivity," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 316-339, April.
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    2. Bouoiyour, Jamal & Selmi, Refk, 2014. "How Does Exchange Rate Uncertainty interact with International Trade? A Meta-Analysis Revisited," MPRA Paper 56201, University Library of Munich, Germany.

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    More about this item

    Keywords

    Meta-regression; meta-analysis; publication bias; data mining; Monte Carlo simulatio;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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