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. (J Appl Econom 28:777–795, 2013) and Harvey (Dynamic models for volatility and heavy tails: with applications to financial and economic time series, Cambridge University Press, Cambridge, 2013). We demonstrate the validity of the proposed model by a Monte Carlo experiment and an application to two real time series.

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

Bisaglia L.;Grigoletto M.
2021

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. (J Appl Econom 28:777–795, 2013) and Harvey (Dynamic models for volatility and heavy tails: with applications to financial and economic time series, Cambridge University Press, Cambridge, 2013). We demonstrate the validity of the proposed model by a Monte Carlo experiment and an application to two real time series.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3392093
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