Bayesian Mode Inference for Discrete Distributions in Economics and Finance

Jamie Cross, Lennart Hoogerheide, Paul Labonne and Herman van Dijk
Additional contact information
Lennart Hoogerheide: Vrije Universiteit Amsterdam
Paul Labonne: Norwegian Business School

No 23-038/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: Detecting heterogeneity within a population is crucial in many economic and financial applications. Econometrically, this requires a credible determination of multimodality in a given data distribution. We propose a straightforward yet effective technique for mode inference in discrete data distributions which involves fitting a mixture of novel shifted-Poisson distributions. The credibility and utility of our proposed approach is demonstrated through empirical investigations on datasets pertaining to loan default risk and inflation expectations.

Keywords: Bayesian Inference; Mixture Models; Mode Inference; Multimodality; Shifted-Poisson. (search for similar items in EconPapers)
JEL-codes: C11 C25 C81 C82 D00 E00 (search for similar items in EconPapers)
Date: 2023-06-29
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Bayesian mode inference for discrete distributions in economics and finance (2024)
Working Paper: Bayesian Mode Inference for Discrete Distributions in Economics and Finance (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20230038

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