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.

中文翻译：

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长时序列预测的新时变模型

在这项工作中，我们提出了一类带有时变分数参数的长记忆模型。特别是，长记忆系数d的动态是通过由预测可能性的分数驱动的随机递归方程来确定的，正如Creal等人所建议的那样。（J Appl Econom 28：777–795，2013）和Harvey（波动率和重尾的动态模型：金融和经济时间序列的应用，剑桥大学出版社，剑桥，2013年）。我们通过蒙特卡洛实验证明了所提出模型的有效性，并将其应用于两个实时序列。