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Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks

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Author Info
Gary Koop () (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy)
Markus Jochmann (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy)
Rodney W. Strachan (University of Queensland, UK and The Rimini Centre for Economic Analysis, Italy)

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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 which allows for coefficients in a possibly over-parameterized VAR to be set to zero. The second allows for an unknown number of structual breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macro-economic data set. We find that, in-sample, these extensions clearly are warranted. 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 the inclusion of breaks. Classification-JEL:

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Publisher Info
Paper provided by Rimini Centre for Economic Analysis in its series Working Paper Series with number 19-08.

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Date of creation: Jan 2008
Date of revision: Jan 2008
Handle: RePEc:rim:rimwps:19-08

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