Arbeitspapier
Combining Predictive Densities Using Bayesian Filtering with Applications to Us Economics Data
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 forevaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
- ISBN
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978-82-7553-586-1
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 2010/29
- Classification
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Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Forecasting Models; Simulation Methods
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Subject
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Bayesian filtering
sequential Monte Carlo
density forecast combination
survey forecast
- Event
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Geistige Schöpfung
- (who)
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Billio, Monica
Casarin, Roberto
Ravazzolo, Francesco
van Dijk, Herman K.
- Event
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Veröffentlichung
- (who)
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Norges Bank
- (where)
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Oslo
- (when)
-
2010
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Billio, Monica
- Casarin, Roberto
- Ravazzolo, Francesco
- van Dijk, Herman K.
- Norges Bank
Time of origin
- 2010
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