The Library
Adaptive models and heavy tails with an application to inflation forecasting
Tools
Tools
Tools
Delle Monache, Davide and Petrella, Ivan (2017) Adaptive models and heavy tails with an application to inflation forecasting. International Journal of Forecasting, 33 (2). pp. 482-501. doi:10.1016/j.ijforecast.2016.11.007
|
PDF
WRAP-adaptive-models-tails-inflation-Petrella-2017.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1426Kb) | Preview |
Official URL: https://doi.org/10.1016/j.ijforecast.2016.11.007
Abstract
This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, the resulting model is observation-driven and is estimated by classical methods. In particular, we consider time variation in both coefficients and volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters so as to attain local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics with the following results: allowing for heavy tails leads to significant improvements in terms of fit and forecast, and the adoption of the Student-t distribution proves to be crucial in order to obtain well calibrated density forecasts. These results are obtained using the US CPI inflation rate and are confirmed by other inflation indicators, as well as for CPI inflation of the other G7 countries.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
| Journal or Publication Title: | International Journal of Forecasting | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 0169-2070 | ||||||||
| Official Date: | April 2017 | ||||||||
| Dates: |
|
||||||||
| Date of first compliant deposit: | 30 November 2016 | ||||||||
| Volume: | 33 | ||||||||
| Number: | 2 | ||||||||
| Page Range: | pp. 482-501 | ||||||||
| DOI: | 10.1016/j.ijforecast.2016.11.007 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
