Forecasting with Medium and Large Bayesian VARS
Koop, Gary (2010) Forecasting with Medium and Large Bayesian VARS. Discussion paper. University of Strathclyde, Glasgow.
Preview |
Text.
Filename: Koop_DPIE_2011_forecasting_with_medium_and_large_bayesian_VARS.pdf
Final Published Version Download (273kB)| Preview |
Abstract
This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We find that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.
ORCID iDs
Koop, Gary ORCID: https://orcid.org/0000-0002-6091-378X;-
-
Item type: Monograph(Discussion paper) ID code: 67935 Dates: DateEventFebruary 2010PublishedNotes: Discussion paper. Subjects: Social Sciences > Economic Theory Department: Strathclyde Business School > Economics Depositing user: Pure Administrator Date deposited: 21 May 2019 14:57 Last modified: 03 Oct 2024 00:10 URI: https://strathprints.strath.ac.uk/id/eprint/67935