Combining Predictive Densities Using Bayesian Filtering with Applications to US Economic Data
40 Pages Posted: 28 Jul 2012 Last revised: 3 Oct 2012
Date Written: July 27, 2012
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.
Keywords: Density Forecast Combination, Survey Forecast, Bayesian Filtering, Sequential Monte Carlo
JEL Classification: C11, C15, C53, E3
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