Bayesian Forecast Combination for VAR Models
Michael K Andersson () and
Sune Karlsson ()
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Michael K Andersson: Department of Business, Economics, Statistics and Informatics, Postal: Örebro University, Department of Business, Economics, Statistics and Informatics, SE - 701 82 ÖREBRO, Sweden
No 2007:13, Working Papers from Örebro University, School of Business
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 di erence 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 di ering sets of endogenous variables. This is a dicult 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)
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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:oruesi:2007_013
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