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Erscheinungsjahr: 
2011
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. 11-003/4
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
Schlagwörter: 
Density Forecast Combination
Survey Forecast
Bayesian Filtering
Sequential Monte Carlo
JEL: 
C11
C15
C53
E37
Dokumentart: 
Working Paper
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