Delle Monache, D. and Petrella, Ivan (2014) Adaptive models and heavy tails. Working Paper. Birkbeck College, University of London, London, UK.
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Abstract
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.
Metadata
| Item Type: | Monograph (Working Paper) |
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| Additional Information: | ISSN 1745-8587: BWPEF 1409 |
| Keyword(s) / Subject(s): | Time-Varying Parameters, Score-driven Models, Heavy-Tails, Adaptive Algorithms, Inflation |
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Depositing User: | Administrator |
| Date Deposited: | 20 May 2016 13:38 |
| Last Modified: | 02 Aug 2023 17:24 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/15288 |
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