Overview
Dale J. Poirier is a Bayesian econometrician (University of California, Irvine), known for foundational textbook and methodological work on Bayesian inference in econometrics — including identification under partial-information priors, Bayesian semiparametrics, and qualitative-response models — and for advocacy of the Bayesian approach within mainstream econometrics.
Key Contributions / Features
- Intermediate Statistics and Econometrics: A Comparative Approach (MIT Press, 1995): a standard Bayesian-vs-classical reference.
- Bayesian semiparametrics (Koop-Poirier 2004; Koop-Poirier-Tobias 2003): natural-conjugate smoothness-prior methods for the partial linear model and multiple-equation systems.
- Partial-information priors and identification (Poirier 1998): analysis of what data can and cannot update when parameters are only partially identified.
- Bayesian logit / qualitative choice (Koop-Poirier 1993): natural-conjugate priors for logit models.
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