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Variational Bayes inference in high-dimensional time-varying parameter models

Gary Koop () and Dimitris Korobilis

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive inference in time-varying parameter models. Our approach involves: i) computationally trivial Kalman filter updates of regression coefficients, ii) a dynamic variable selection prior that removes irrelevant variables in each time period, and iii) a fast approximate state-space estimator of the regression volatility parameter. In an exercise involving simulated data we evaluate the new algorithm numerically and establish its computational advantages. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts over a number of alternatives.

Keywords: dynamic linear model; approximate posterior inference; dynamic variable selection; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C13 C52 C53 C61 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ore
Date: 2018-07-15
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https://mpra.ub.uni-muenchen.de/87972/1/MPRA_paper_87972.pdf original version (application/pdf)

Related works:
Working Paper: Variational Bayes inference in high-dimensional time-varying parameter models (2018) Downloads
Working Paper: Variational Bayes inference in high-dimensional time-varying parameter models (2018) Downloads
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