Improved Marginal Likelihood Estimation via Power Posteriors and Importance Sampling
Yong Li (),
Nianling Wang () and
Jun Yu ()
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Yong Li: Renmin University of China
Nianling Wang: Renmin University of China
No 16-2019, Economics and Statistics Working Papers from Singapore Management University, School of Economics
The power-posterior method of Friel and Pettitt (2008) has been used to estimate the marginal likelihoods of competing Bayesian models. In this paper it is shown that the Bernstein-von Mises (BvM) theorem holds for the power posteriors under regularity conditions. Due to the BvM theorem, the power posteriors, when adjusted by the square root of the corresponding grid points, converge to the same normal distribution as the original posterior distribution, facilitating the implementation of importance sampling for the purpose of estimating the marginal likelihood. Unlike the power-posterior method that requires repeated posterior sampling from the power posteriors, the new method only requires the posterior output from the original posterior. Hence, it is computationally more efficient to implement. Moreover, it completely avoids the coding efforts associated with drawing samples from the power posteriors. Numerical efficiency of the proposed method is illustrated using two models in economics and finance.
Keywords: Bayes factor; Marginal likelihood; Markov Chain Monte Carlo; Model choice; Power posteriors; Importance sampling (search for similar items in EconPapers)
JEL-codes: C11 C12 (search for similar items in EconPapers)
Pages: 38 pages
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2019_016
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