A Pragmatic Method for Model-Selection Based on the Widely Applicable Bayesian Information Criterion

Hiroyuki Watanabe
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Hiroyuki Watanabe: Research Institute for Economics & Business Administration (RIEB), Kobe University, Japan

No DP2017-20, Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University

Abstract: In this study, we discuss the use of the Widely Applicable Bayesian Information Criterion (WBIC) when prior information is unknown. We provide a numerical example whereby if the prior is arbitrarily set to be tight or weak, the marginal likelihood can fail to find the best econometric model, even though its likelihood function is consistent with true data-generating process. Given this fact, we propose combining WBIC and a noninformative prior. This procedure objectively selects econometric models in a Bayesian context, and yields a reasonable result in such a situation.

Keywords: Bayesian information criterion; Marginal likelihood; Markov-switching model; Noninformative prior; WBIC (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2017-08
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:kob:dpaper:dp2017-20

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