ESTIMATION IN LONG‐MEMORY TIME SERIES MODEL
ESTIMATION IN LONG‐MEMORY TIME SERIES MODEL
复制标题
长记忆时间序列模型中的估计
DOI：
10.1111/j.1467-9892.1988.tb00451.x
复制
发表时间：
1988
影响因子：
0.9
通讯作者：
K. Eom
中科院分区：
文献类型：
--
作者：
R. Kashyap;K. Eom
本文研究了长记忆时间序列模型的参数估计问题。提出了一个无偏一致估计量。该估计方法基于频域最小二乘法，计算简单。同时，导出了Cramer-Rao下界。该估计器的均方误差为O（1/N）阶，其中N为样本数。利用合成的长记忆时间序列数据验证了估计的准确性。
This study deals with the parameter estimation in long-memory time series models. An unbiased and consistent estimator is proposed. The proposed estimator is based on a least-squares method in the frequency domain, and it is computationally simple. Also, the Cramer–Rao lower bound is derived. The mean-square error of the proposed estimator is order of O(1/N), where N is the number of samples. The accuracy of the estimates is verified using synthetic long-memory time series data.