Semiparametric Whittle estimation of a cyclical long-memory time series based on generalised exponential models
Semiparametric Whittle estimation of a cyclical long-memory time series based on generalised exponential models
复制标题
基于广义指数模型的循环长记忆时间序列的半参数Whittle估计
DOI：
10.1080/10485252.2016.1163350
复制
发表时间：
2016
影响因子：
1.2
通讯作者：
M.
中科院分区：
文献类型：
--
作者：
Narukawa;M.
本文利用基于广义指数（GEXP）模型的Whittle似然，研究了周期长记忆时间序列中记忆参数的半参数估计，该序列表现出对周期行为的强烈依赖性。所提出的估计包含在所谓的宽带或全局方法中，并使用来自所有频率的频谱密度的信息。我们建立了线性过程估计的记忆参数的一致性和渐近正态性，因此不需要高斯性。通过蒙特卡罗实验进行的仿真研究表明，与现有的半参数估计相比，所提出的估计效果较好。此外，我们对所提出的估计进行了实证应用，将其应用于日本工业生产指数的增长率并检测其周期性持久性。
This paper considers a semiparametric estimation of the memory parameter in a cyclical long-memory time series, which exhibits a strong dependence on cyclical behaviour, using the Whittle likelihood based on generalised exponential (GEXP) models. The proposed estimation is included in the so-called broadband or global method and uses information from the spectral density at all frequencies. We establish the consistency and the asymptotic normality of the estimated memory parameter for a linear process and thus do not require Gaussianity. A simulation study conducted using Monte Carlo experiments shows that the proposed estimation works well compared to other existing semiparametric estimations. Moreover, we provide an empirical application of the proposed estimation, applying it to the growth rate of Japan's industrial production index and detecting its cyclical persistence.