Combining Predictive Densities Using Bayesian Filtering with Applications to US Economics Data
Ca Foscari University of Venice - Department of Economics
University of Brescia - Department of Economics; University of Venice - GRETA Ass.; Université Paris Dauphine - CEREMADE
H. K. Van Dijk
Tinbergen Institute; Econometric Institute
December 20, 2010
Norges Bank Working Paper No. 2010/29
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.
Number of Pages in PDF File: 41
Keywords: Density Forecast Combination, Survey Forecast, Bayesian Filtering, Sequential Monte Carlo
JEL Classification: C11, C15, C53, E37working papers series
Date posted: January 9, 2011
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