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Join date: Nov 15, 2018

About

Tomas Havranek is Professor at the Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague. Prior to that appointment he was Advisor to the Board at the Czech National Bank. His research interests include international trade, macroeconomics, monetary policy, energy economics, and methodology of research synthesis. He has published, among other outlets, in the Review of Economics and Statistics, Journal of the European Economic Association, and Journal of International Economics. According to RePEc he is currently the most cited Czech economist. More information is available at tomashavranek.cz and meta-analysis.cz.

Posts (11)

Sep 2, 2026 ∙ 3 min
Rebuilding meta-analysis.cz: what is on it now
This summer our website meta-analysis.cz was hacked (thank you, AI!). It was our own fault. We hadn't changed the site in any serious way since 2015 and were still uploading files by FTP, one paper at a time. There was no central backup. Rebuilding it by hand would have taken months. Instead we reconstructed it with Claude Code and Codex (thank you, AI!), working from old copies on the Wayback Machine and whatever we could still find on our hard drives. That took a few days. Cleaning up...

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Jul 28, 2026 ∙ 2 min
Two new pre-registered papers: outlier decisions in meta-analysis and AI feedback on meta
My colleagues and I have two new pre-registered papers that may interest MAER-Net members. 1. Do decisions about outliers and influential effects matter? (https://meta-analysis.cz/outliers, https://arxiv.org/abs/2607.23174) With Zuzana Irsova, Martina Luskova, and Tom Stanley, we recompute 358 behavioral science meta-analyses under five outlier treatments: do nothing, drop the most extreme estimate, remove studentized residuals above 3, winsorize at 5/95, and remove estimates with |DFBETAS|...

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May 31, 2026 ∙ 1 min
Stress-testing research with AI, now super easy and fully automated
Last December we shared a protocol for stress-testing (meta-)research by making AI models argue and keeping what survives. But running it by hand (opening several models, copying outputs back and forth) is a chore and our original automation via a GPT agent was not reliable. So we automated the protocol using Claude Code. After a one-time setup it is a single sentence: you describe the task, and the skill (built with Zuzana Irsova) has Claude call OpenAI's Codex, runs the critique and...

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Tomas Havranek

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