Forecasting with dimension switching VARs
Koop, Gary (2014) Forecasting with dimension switching VARs. International Journal of Forecasting, 30 (2). pp. 280-290. ISSN 0169-2070 (https://doi.org/10.1016/j.ijforecast.2013.09.005)
Preview |
Text.
Filename: koop_IJF2014_Forecasting_with_dimension_switching_VARs.pdf
Accepted Author Manuscript License: Download (157kB)| Preview |
Abstract
This paper develops methods for VAR forecasting when the researcher is uncertain about which variables enter the VAR, and the dimension of the VAR may be changing over time. It considers the case where there are N variables which might potentially enter a VAR and the researcher is interested in forecasting N ∗ of them. Thus, the researcher is faced with 2 N − N ∗ potential VARs. If N is large, conventional Bayesian methods can be infeasible due to the computational burden of dealing with a huge model space. Allowing for the dimension of the VAR to change over time only increases this burden. In light of these considerations, this paper uses computationally practical approximations adapted from the dynamic model averaging literature in order to develop methods for dynamic dimension selection (DDS) in VARs. We then show the benefits of DDS in a macroeconomic forecasting application. In particular, DDS switches between different parsimonious VARs and forecasts appreciably better than various small and large dimensional VARs.
ORCID iDs
Koop, Gary ORCID: https://orcid.org/0000-0002-6091-378X;-
-
Item type: Article ID code: 52341 Dates: DateEventApril 2014Published22 December 2013Published OnlineNotes: NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, VOL 30, ISSUE 2, (April–June 2014) DOI 10.1016/j.ijforecast.2013.09.005 Subjects: Social Sciences > Economic Theory Department: Strathclyde Business School > Economics Depositing user: Pure Administrator Date deposited: 31 Mar 2015 07:22 Last modified: 11 Nov 2024 11:02 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/52341