Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis

Hugo Storm, Thomas Heckelei and Kathy Baylis

European Review of Agricultural Economics, 2024, vol. 51, issue 3, 589-616

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.

Keywords: probabilistic programming; Bayesian inference; machine learning; econometrics; quantitative economic analysis (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1093/erae/jbae016 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:erevae:v:51:y:2024:i:3:p:589-616.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

European Review of Agricultural Economics is currently edited by Timothy Richards, Salvatore Di Falco, Céline Nauges and Vincenzina Caputo

More articles in European Review of Agricultural Economics from Oxford University Press and the European Agricultural and Applied Economics Publications Foundation Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().