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

Lookup NU author(s): Dr Markus Jochmann

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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.

Publication metadata

Author(s): Jochmann M, Koop G, Strachan R

Publication type: Article

Publication status: Published

Journal: International Journal of Forecasting

Year: 2010

Volume: 26

Issue: 2

Pages: 326-347

Print publication date: 04/02/2010

ISSN (print): 0169-2070

ISSN (electronic): 1872-8200

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.ijforecast.2009.11.002

DOI: 10.1016/j.ijforecast.2009.11.002

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