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Adaptive models and heavy tails with an application to inflation forecasting

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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

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Official URL: https://doi.org/10.1016/j.ijforecast.2016.11.007

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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:
DateEvent
April 2017Published
5 February 2017Available
29 November 2016Accepted
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
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