Hybrid elicitation and indirect Bayesian inference with quantile-parametrized likelihood

Dmytro Perepolkin, Benjamin Goodrich and Ullrika Sahlin

No paby6, OSF Preprints from Center for Open Science

Abstract: This paper extends the application of indirect Bayesian inference to probability distributions defined in terms of quantiles of the observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and interpretability of its parameters, and are therefore useful for elicitation on observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a version of the Dirichlet prior. The resulting “hybrid” expert elicitation protocol for characterizing uncertainty in parameters using questions about the observable quantities is discussed and contrasted to parametric and predictive elicitation.

Date: 2021-09-28
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:paby6

DOI: 10.31219/osf.io/paby6

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