Arbeitspapier
Adaptive models and heavy tails
This paper proposes a novel and flexible framework to estimate autoregressive models with time-varying parameters. Our setup nests various adaptive algorithms that are commonly used in the macroeconometric literature, such as learning-expectations and forgetting-factor algorithms. These are generalized along several directions: specifically, we allow for both Student-t distributed innovations as well as time-varying volatility. Meaningful restrictions are imposed to the model parameters, so as to attain local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics. Allowing for heavy-tails leads to a significant improvement in terms of fit and forecast. Moreover, it proves to be crucial in order to obtain well-calibrated density forecasts.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 720
- Classification
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
Forecasting Models; Simulation Methods
Price Level; Inflation; Deflation
- Subject
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Time-varying parameters
Score-driven models
Heavy-tails
Adaptive algorithms
Inflation
- Event
-
Geistige Schöpfung
- (who)
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Delle Monache, Davide
Petrella, Ivan
- Event
-
Veröffentlichung
- (who)
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Queen Mary University of London, School of Economics and Finance
- (where)
-
London
- (when)
-
2014
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
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
- Delle Monache, Davide
- Petrella, Ivan
- Queen Mary University of London, School of Economics and Finance
Time of origin
- 2014
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