Adaptive Models and Heavy Tails

Davide Delle Monache and Ivan Petrella

No 720, Working Papers from Queen Mary University of London, School of Economics and Finance

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.

Keywords: Time-varying parameters; Score-driven models; Heavy-tails; Adaptive algorithms; Inflation (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 E31 (search for similar items in EconPapers)
Date: 2014-07-01
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Adaptive models and heavy tails (2016)
Working Paper: Adaptive models and heavy tails (2016)
Working Paper: Adaptive Models and Heavy Tails (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:720

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