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

Markus Jochmann (), Gary Koop and Rodney Strachan

International Journal of Forecasting, 2010, vol. 26, issue 2, 326-347

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 VARÂ to be set to zero. The second extension allows for an unknown number of structural breaks in the VARÂ 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.

Keywords: Vector; autoregressive; model; Predictive; density; Over-parameterization; Structural; break; Shrinkage (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (40)

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Related works:
Working Paper: Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:26:y::i:2:p:326-347

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