Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/122073
Authors: 
Delle Monache, Davide
Petrella, Ivan
Year of Publication: 
2014
Series/Report no.: 
Working Paper, School of Economics and Finance, Queen Mary, University of London 720
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.
Subjects: 
Time-varying parameters
Score-driven models
Heavy-tails
Adaptive algorithms
Inflation
JEL: 
C22
C51
C53
E31
Document Type: 
Working Paper

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