本文提出了一个新的方法来估计SARFIMA模型的分数差分参数，即通过采用分割余弦锥化函数方法来改进EGPH估计。模拟结果给出了采用分割余弦锥化函数方法估计EGPH的最优分割比例和最优带宽。新方法优于EGPH和带有余弦锥化函数的EGPH方法。论文进一步应用改进的EGPH方法估计日内交易量序列和高频绝对收益序列，结果显示季节分数差分参数都很大，而非季节分数差分参数都很小。这表明时间序列的长记忆性质可能是由于数据本身的季节长程相关结构决定的。

Abstract

This paper presents a new method to estimate the fractional differencing parameters in the SARFIMA model. A technique of split cosine bell tapering is suggested to improve the EGPH method. The simulation study shows that the optimal split proportion and bandwidth for the EGPH with split cosine bell tapering method respectively are p = 0.1 and b = 0.9. The new method with the optimal parameters outperforms the EGPH and EGPH with cosine bell tapering. We further applied the EGPH method to estimate intraday volume series and high frequency absolute return data. The results show that the seasonal fractionally differencing parameters are all estimated to be large, while the nonseasonal fractionally differencing parameters are all very small. This indicates that their long memory property may be mainly caused by the structure of long-range dependence at the seasonal lags instead of dependence at the nonseasonal lags.

Xunyu Ye, Ping Gao, Handong Li (correspondent author). Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram [J]. Economic Modelling 2015, 46: 167–179.