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

Full text not available in this repository.Request a copy from the Strathclyde author

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.

ORCID iDs

Jochmann, Markus, Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X and Strachan, Rodney W.;
  • Item type:Article
    ID code:14555
    Dates:
    Date
    Event
    April 2010
    Published
    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
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/14555
CORE (COnnecting REpositories)