Bayesian forecast combination for VAR models
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.
|Date of creation:||01 Nov 2007|
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"Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach,"
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"Forecast Combination and Model Averaging using Predictive Measures,"
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"Numerical Methods for Estimation and Inference in Bayesian VAR-Models,"
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"Benchmark priors for Bayesian model averaging,"
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26, Edinburgh School of Economics, University of Edinburgh.
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- Kapetanios, George & Labhard, Vincent & Price, Simon, 2008.
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""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates","
90-92-23, California Irvine - School of Social Sciences.
- Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
- Bernanke, Ben S. & Boivin, Jean, 2003.
"Monetary policy in a data-rich environment,"
Journal of Monetary Economics,
Elsevier, vol. 50(3), pages 525-546, April.
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