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Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis
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DOI：10.1093/erae/jbae016.png)
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Abstract
En 中文
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
Journal
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3.5
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1.4K
Citations:
2.2K
