An Automated Prior Robustness Analysis in Bayesian Model Comparison

28 Pages Posted: 25 Jun 2019

See all articles by Joshua C. C. Chan

Joshua C. C. Chan

University of Technology Sydney (UTS) - UTS Business School

Liana Jacobi

University of Melbourne - Faculty of Business and Economics; IZA Institute of Labor Economics

Dan Zhu

Monash University - Department of Econometrics & Business Statistics

Date Written: June 25, 2019

Abstract

[enter Abstract BThe 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

JEL Classification: C11, C53, E37

Suggested Citation

Chan, Joshua C. C. and Jacobi, Liana and Zhu, Dan, An Automated Prior Robustness Analysis in Bayesian Model Comparison (June 25, 2019). CAMA Working Paper No. 45/2019. Available at SSRN: https://ssrn.com/abstract=3409549 or http://dx.doi.org/10.2139/ssrn.3409549

Joshua C. C. Chan (Contact Author)

University of Technology Sydney (UTS) - UTS Business School ( email )

Sydney
Australia

Liana Jacobi

University of Melbourne - Faculty of Business and Economics ( email )

Victoria, 3010
Australia

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

Dan Zhu

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia

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