Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87159
Authors: 
Billio, Monica
Casarin, Roberto
Ravazzolo, Francesco
van Dijk, Herman K.
Year of Publication: 
2012
Series/Report no.: 
Tinbergen Institute Discussion Paper 12-118/III
Abstract: 
We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to approximate the filtering and predictive densities. The combination approach is assessed using statistical and utility-based performance measures for evaluating density forecasts. Simulation results indicate that, for a set of linear autoregressive models, the combination strategy is successful in selecting, with probability close to one, the true model when the model set is complete and it is able to detect parameter instability when the model set includes the true model that has generated subsamples of data. For the macro series we find that incompleteness of the models is relatively large in the 70's, the beginning of the 80's and during the recent financial crisis, and lower during the Great Moderation. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 90's and switches to giving more weight to the professional forecasts over time.
Subjects: 
Density Forecast Combination
Survey Forecast
Bayesian Filtering
Sequential Monte Carlo
JEL: 
C11
C15
C53
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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