An automated prior robustness analysis in Bayesian model comparison

Joshua Chan, Liana Jacobi and Dan Zhu

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: The marginal likelihood is the gold standard for Bayesian model comparison although it is well-known that the value of marginal likelihood could be sensitive to the choice of prior hyperparameters. Most models require computationally intense simulation-based methods to evaluate the typically high-dimensional integral of the marginal likelihood expression. Hence, despite the recognition that prior sensitivity analysis is important in this context, it is rarely done in practice. In this paper we develop efficient and feasible methods to compute the sensitivities of marginal likelihood, obtained via two common simulation-based methods, with respect to any prior hyperparameter alongside the MCMC estimation algorithm. Our approach builds on Automatic Differentiation (AD), which has only recently been introduced to the more computationally intensive setting of Markov chain Monte Carlo simulation. We illustrate our approach with two empirical applications in the context of widely used multivariate time series models.

Keywords: automatic differentiation; model comparison; vector autoregression; factor models (search for similar items in EconPapers)
JEL-codes: C11 C53 E37 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2019-06
New Economics Papers: this item is included in nep-cmp, nep-dcm, nep-ecm, nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: An automated prior robustness analysis in Bayesian model comparison (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2019-45

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