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A new time-varying model for forecasting long-memory series

Luisa Bisaglia and Matteo Grigoletto

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Abstract: In this work we propose a new class of long-memory models with time-varying fractional parameter. In particular, the dynamics of the long-memory coefficient, $d$, is specified through a stochastic recurrence equation driven by the score of the predictive likelihood, as suggested by Creal et al. (2013) and Harvey (2013). We demonstrate the validity of the proposed model by a Monte Carlo experiment and an application to two real time series.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2018-12
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