Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks

Jochmann, Markus and Koop, Gary and Strachan, Rodney W. (2010) Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks. International Journal of Forecasting, 26 (2). pp. 326-347. ISSN 0169-2070

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Abstract

This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device that allows coefficients in a possibly over-parameterized VAR to be set to zero. The second extension allows for an unknown number of structural breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macroeconomic data set. In a recursive forecasting exercise, we find moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than to the inclusion of breaks.

Creators(s): Jochmann, Markus, Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X and Strachan, Rodney W.;
Item type:Article
ID code:14555
Notes:Also available as a working paper (2008): http://personal.strath.ac.uk/gary.koop/jochmann_koop_strachan.pdf
Keywords:vector autoregressive model, predictive density, over-parameterization, structural break, shrinkage, Commerce, Business and International Management
Subjects:Social Sciences > Commerce
Department:Strathclyde Business School > Economics
Depositing user: Strathprints Administrator
Date deposited:01 Apr 2010 14:13
Last modified:20 Jan 2021 18:19
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URI:https://strathprints.strath.ac.uk/id/eprint/14555
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