Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/318143 
Year of Publication: 
2016
Series/Report no.: 
BCAM Working Paper No. 1603
Publisher: 
Birkbeck, University of London, Birkbeck Centre for Applied Macroeconomics (BCAM), London
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.
Subjects: 
adaptive algorithms
inflation
score-driven models
student-t
time-varying parameters
JEL: 
C22
C51
C53
E31
Document Type: 
Working Paper

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