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Adaptive Models and Heavy Tails

Listed author(s):
  • Davide Delle Monache

    ()

    (Queen Mary University of London)

  • Ivan Petrella

    ()

    (Birkbeck, University of London and CEPR)

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.

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File URL: http://www.econ.qmul.ac.uk/media/econ/research/workingpapers/2014/items/wp720.pdf
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Paper provided by Queen Mary University of London, School of Economics and Finance in its series Working Papers with number 720.

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Date of creation: Jul 2014
Handle: RePEc:qmw:qmwecw:wp720
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