Bayesian dynamic variable selection in high dimensions

Koop, Gary and Korobilis, Dimitris (2023) Bayesian dynamic variable selection in high dimensions. International Economic Review, 64 (3). ISSN 0020-6598 (https://doi.org/10.1111/iere.12623)

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Abstract

This article addresses the issue of inference in time-varying parameter regression models in the presence of many predictors and develops a novel dynamic variable selection strategy. The proposed variational Bayes dynamic variable selection algorithm allows for assessing at each time period in the sample which predictors are relevant (or not) for forecasting the dependent variable. The algorithm is used to forecast inflation using over 400 macroeconomic, financial, and global predictors, many of which are potentially irrelevant or short-lived. The new methodology is able to ensure parsimonious solutions to this high-dimensional estimation problem, which translate into excellent forecast performance.

ORCID iDs

Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X and Korobilis, Dimitris;
  • Item type:Article
    ID code:83329
    Dates:
    Date
    Event
    7 August 2023
    Published
    19 January 2023
    Published Online
    17 November 2022
    Accepted
    20 September 2020
    Submitted
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:24 Nov 2022 15:58
    Last modified:21 Nov 2024 01:23
    URI:https://strathprints.strath.ac.uk/id/eprint/83329
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