Bayesian Compressed Vector Autoregressions

Gary Koop, Dimitris Korobilis and Davide Pettenuzzo ()

No 103, Working Papers from Brandeis University, Department of Economics and International Business School

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 better than either factor methods or large VAR methods involving prior shrinkage.

Keywords: multivariate time series; random projection; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2016-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP103.pdf (application/pdf)

Related works:
Journal Article: Bayesian compressed vector autoregressions (2019)
Working Paper: Bayesian Compressed Vector Autoregressions (2017)
Working Paper: Bayesian Compressed Vector Autoregressions (2016)
Working Paper: Bayesian Compressed Vector Autoregressions (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:brd:wpaper:103

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