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- W3121597394 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 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 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." @default.
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- W3121597394 date "2008-01-01" @default.
- W3121597394 modified "2023-09-23" @default.
- W3121597394 title "Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks" @default.
- W3121597394 hasPublicationYear "2008" @default.
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