Variational Bayes in State Space Models: Inferential and Predictive Accuracy

David Frazier (), Gael Martin () and Ruben Loaiza-Maya ()
Authors registered in the RePEc Author Service: Rubén Albeiro Loaiza Maya

No 1/22, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that, in terms of accuracy on fixed parameters, there is a clear hierarchy in terms of the methods, with approaches that do not approximate the states yielding superior accuracy over methods that do. We also document numerically that the inferential discrepancies between the various methods often yield only small discrepancies in predictive accuracy over small out-of-sample evaluation periods. Nevertheless, in certain settings, these predictive discrepancies can become meaningful over a longer out-of-sample period. This finding indicates that the invariance of predictive results to inferential inaccuracy, which has been an oft-touted point made by practitioners seeking to justify the use of variational inference, is not ubiquitous and must be assessed on a case-by-case basis.

Keywords: State space models; variational inference; probabilistic forecasting; Bayesian consistency; scoring rules (search for similar items in EconPapers)
Pages: 53
Date: 2022
New Economics Papers: this item is included in nep-for and nep-ore
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Citations: View citations in EconPapers (7)

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Working Paper: Variational Bayes in State Space Models: Inferential and Predictive Accuracy (2022)
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