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Estimate Dependency in Meta-Analysis: A Generalized-Weights Solution to Sample Overlap

  • Date 2017-08-22 (Tue)
  • Time 02:30 PM
  • Venue Conference Room B110
  • Presider Professor Yu-Chin Hsu
  • Speaker Professor Heiko Rachinger
  • Background Professor Rachinger received his Ph.D. from Charles III University of Madrid in 2012. He is currently an Assistant Professor of Economics at the University of Vienna. His research interests include econometrics, macroeconometrics, and finance.
  • Abstract A common feature of meta-analyses in economics, especially in macroeconomics and related subfields, is that the samples underlying the reported effect sizes overlap. The resulting positive correlation between effect sizes decreases the efficiency of standard meta-estimation methods. Ignoring sample overlap generates downward-biased standard errors and, thus, invalid meta-inference. This paper argues that the variance-covariance matrix describing the structure of dependency between primary estimates can be feasibly specified as function of information that is typically reported in the primary studies. Meta-estimation efficiency can then be enhanced by using the resulting matrix in a Generalized Least Squares fashion. Our simulations illustrate efficiency losses and size distortions potentially arising from empirically-relevant sample overlap scenarios.