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

Stephan B. Bruns ()
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Stephan B. Bruns: Max Planck Institute of Economics, Jena

No 2013-040, Jena Economics Research Papers from Friedrich-Schiller-University 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.

Keywords: Meta-regression; meta-analysis; publication bias; data mining; Monte Carlo simulatio (search for similar items in EconPapers)
JEL-codes: C12 C15 C40 (search for similar items in EconPapers)
Date: 2013-09-27
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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