Subspace shrinkage in conjugate Bayesian vector autoregressions

Florian Huber and Gary Koop

Journal of Applied Econometrics, 2023, vol. 38, issue 4, 556-576

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.

Date: 2023
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https://doi.org/10.1002/jae.2966

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Working Paper: Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions (2021)
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