Subspace shrinkage in conjugate Bayesian vector autoregressions

Huber, Florian and Koop, Gary (2023) Subspace shrinkage in conjugate Bayesian vector autoregressions. Journal of Applied Econometrics, 38 (4). pp. 556-576. ISSN 0883-7252 (https://doi.org/10.1002/jae.2966)

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Abstract

Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR with a subspace shrinkage prior that combines the two. This prior shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage and the number of factors. After establishing the theoretical properties of our prior, we show that it successfully detects the number of factors in simulations and that it leads to forecast improvements using US macroeconomic data.

ORCID iDs

Huber, Florian and Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X;
  • Item type:Article
    ID code:82578
    Dates:
    Date
    Event
    31 July 2023
    Published
    6 February 2023
    Published Online
    23 September 2022
    Accepted
    21 October 2021
    Submitted
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:05 Oct 2022 09:47
    Last modified:11 Nov 2024 13:38
    URI:https://strathprints.strath.ac.uk/id/eprint/82578
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