Bayesian dynamic variable selection in high dimensions

Dimitris Korobilis and Gary Koop

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large number of predictors. This strategy allows for assessing in individual time periods which predictors are relevant (or not) for forecasting the dependent variable. The new algorithm is evaluated numerically using synthetic data and its computational advantages are established. 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 of price inflation over a number of alternative forecasting models.

Keywords: dynamic linear model; approximate posterior inference; dynamic variable selection; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C22 C52 C53 C61 C87 (search for similar items in EconPapers)
Date: 2020-05-05
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (14)

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Related works:
Journal Article: BAYESIAN DYNAMIC VARIABLE SELECTION IN HIGH DIMENSIONS (2023)
Working Paper: Bayesian dynamic variable selection in high dimensions (2020)
Working Paper: Bayesian dynamic variable selection in high dimensions (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:100164

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