Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data
2011-01-04
Research Paper
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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
Classifications using
Journal of Economic Literature (JEL) Classification System
- C15 : Simulation Methods; Monte Carlo Methods; Bootstrap Methods
- E37 : Forecasting and Simulation
- C11 : Bayesian Analysis
- C53 : Forecasting and Other Model Applications
Automatically Extracted Terms
- density
- forecast
- model
- weight
- combination
- survey
- prediction
- scheme
- yt +1
- forecasting
- density forecasts
- time t
- strategy
- value
- method
- time-varying weights
- dynamic
- time-varying
- point forecasts
- point
