Examinando por Autor "Irsova, Zuzana"

Mostrando 1 - 2 de 2
  • Miniatura
    Ítem
    Conventional wisdom, meta-analysis, and research revision in economics
    (John Wiley and Sons Inc, 2025-05-20) Gechert, Sebastian; Mey, Bianka; Opatrny, Matej; Havránek, Tomáš ; Stanley, T. D.; Bom, Pedro; Doucouliagos, Hristos; Heimberger, Philipp; Irsova, Zuzana; Rachinger, Heiko
    Over the past several decades, meta-analysis has emerged as a widely accepted tool to understand economics research. Meta-analyses often challenge the established conventional wisdom of their respective fields. We systematically review a wide range of influential meta-analyses in economics and compare them to “conventional wisdom.” After correcting for observable biases, the empirical economic effects are typically much closer to zero and sometimes switch signs. Typically, the relative reduction in effect sizes is 45%–60%.
  • Miniatura
    Ítem
    Spurious precision in meta-analysis of observational research
    (Nature Research, 2025-09-26) Irsova, Zuzana; Bom, Pedro; Havránek, Tomáš; Rachinger, Heiko
    Meta-analysis assigns more weight to studies with smaller standard errors to maximize the precision of the overall estimate. In observational settings, however, standard errors are shaped by methodological decisions. These decisions can interact with publication bias and p-hacking, potentially leading to spuriously precise results reported by primary studies. Here we show that such spurious precision undermines standard meta-analytic techniques, including inverse-variance weighting and bias corrections based on the funnel plot. Through simulations and large-scale empirical applications, we find that selection models do not resolve the issue. In some cases, a simple unweighted mean of reported estimates outperforms widely used correction methods. We introduce MAIVE (Meta-Analysis Instrumental Variable Estimator), an approach that reduces bias by using sample size as an instrument for reported precision. MAIVE offers a simple and robust solution for improving the reliability of meta-analyses in the presence of spurious precision.