AI 中文总结
研究提出一类含缓和分数差分项的观测驱动模型（GARTFIMA模型），用部分最大似然法进行参数估计等，经蒙特卡罗模拟和实证应用，展现其在有限样本性能及实际应用中的优势。
AI 中文摘要
本文介绍了一类观测驱动模型，其系统成分包含一个缓和分数差分项。该规范推广了基于分数差分算子的长程相关模型，能实现更通用且稳健的模型规范，具有理论优势。我们提出用于参数估计的部分最大似然方法，并处理假设检验、置信区间、拟合优度评估以及样本内和样本外预测。通过蒙特卡罗模拟研究评估了所提估计方法的有限样本性能，实证应用说明了该模型的实际效用。
英文摘要
This paper introduces a class of observation-driven models whose systematic component includes a tempered fractional differencing term. This specification generalizes long-range dependent models based on the fractional differencing operator, enabling a more general and robust model specification while offering theoretical advantages. We propose a partial maximum likelihood approach for parameter estimation and address hypothesis testing, confidence intervals, goodness-of-fit assessment, and both in-sample and out-of-sample forecasting. A Monte Carlo simulation study evaluates the finite-sample performance of the proposed estimation method, and an empirical application illustrates the model's practical utility.
