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

Luisa Bisaglia () and Matteo Grigoletto ()
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Luisa Bisaglia: University of Padova
Matteo Grigoletto: University of Padova

Statistical Methods & Applications, 2021, vol. 30, issue 1, No 5, 139-155

Abstract: 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.

Keywords: Long-memory; GAS model; Time-varying parameter (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10260-020-00517-7

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