Tomas Havranek | Profile

Profile

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 (7)

Oct 31, 2025 ∙ 2 min

Highlights from the 2025 MAER-Net Colloquium in Ottawa

by Tom Stanley, Sebastian Gechert, Jerome Geyer-Klingeberg, and Tomas Havranek The Ottawa Colloquium was a wonderful experience and a great success. The quality of presentations and the richness of discussions have never been better. AI featured prominently this year, sparking lively debates that continue on MAER-Net’s blog . We also had many methodological contributions, each offering new ways to understand economics research or to avoid misinterpretation. Of particular note were the...

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Jul 30, 2025 ∙ 4 min

AI Tools for Meta-Analysis

AI tools now significantly reduce the workload in meta-analysis, especially for literature search and data collection. ChatGPT’s deep research and Agent functions streamline screening and extraction, while tools like ASReview and NotebookLM offer robust validation. Human oversight remains essential, but fewer co-authors are needed than before. Claude also shows promise in reasoning, though I haven’t tested it systematically yet.

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Jul 7, 2025 ∙ 2 min

New Challenges for Meta-Analysis: Attenuation Bias, P-Hacking, Preferred Estimates

Our recent meta-analyses highlight three issues for the field: attenuation bias can rival publication bias in distorting results; new methods like MAIVE address p-hacking more effectively; and author “preferred” estimates may systematically differ from others. Each has implications for best practices in meta-analysis.

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