Bayesian compressed vector autoregressions

Koop, Gary and Korobilis, Dimitris and Pettenuzzo, Davide (2019) Bayesian compressed vector autoregressions. Journal of Econometrics, 210 (1). pp. 135-154. ISSN 0304-4076

[thumbnail of Koop-etal-JE-2017-Bayesian-compressed-vector-autoregressions]
Preview
Text (Koop-etal-JE-2017-Bayesian-compressed-vector-autoregressions)
Koop_etal_JE_2017_Bayesian_compressed_vector_autoregressions.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (1MB)| Preview

    Abstract

    Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR methods to forecast as well or better than either factor methods or large VAR methods involving prior shrinkage.

    ORCID iDs

    Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X, Korobilis, Dimitris and Pettenuzzo, Davide;