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Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis

Author

Listed:
  • Hugo Storm
  • Thomas Heckelei
  • Kathy Baylis

Abstract

The replication crisis in empirical research calls for a more mindful approach to how we apply and report statistical models. For empirical research to have a lasting (policy) impact, these concerns are crucial. In this paper, we present Probabilistic Programming (PP) as a way forward. The PP workflow with an explicit data-generating process enhances the communication of model assumptions, code testing and consistency between theory and estimation. By simplifying Bayesian analysis, it also offers advantages for the interpretation, communication and modelling of uncertainty. We outline the advantages of PP to encourage its adoption in our community.

Suggested Citation

  • Hugo Storm & Thomas Heckelei & Kathy Baylis, 2024. "Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 51(3), pages 589-616.
    • Handle: RePEc:oup:erevae:v:51:y:2024:i:3:p:589-616.
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    File URL: http://hdl.handle.net/10.1093/erae/jbae016
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