Bayesian forecast combination for VAR models
Michael K Andersson () and
Sune Karlsson ()
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Michael K Andersson: Monetary Policy Department, Central Bank of Sweden, Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
No 216, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)
We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty regarding which endogenous variables to include in the model. That is, all models include the forecast variables, but may otherwise have differing sets of endogenous variables. This is a difficult problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the variables of interest and we construct model weights from the predictive likelihood of the forecast variables. The procedure is evaluated in a small simulation study and found to perform competitively in applications to real world data.
Keywords: Bayesian model averaging; Predictive likelihood; GDP forecasts (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Working Paper: Bayesian Forecast Combination for VAR Models (2007)
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Persistent link: http://EconPapers.repec.org/RePEc:hhs:rbnkwp:0216
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