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首页> 外文期刊>Journal of simulation >Applicability of the Whittle estimator to non-stationary and non-linear long-memory processes
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Applicability of the Whittle estimator to non-stationary and non-linear long-memory processes

机译：Whittle估计器对非平稳和非线性长存储过程的适用性

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摘要

The Whittle estimator is a classic statistic to measure the long-memory parameter of stationary stochastic processes. Recently, the theoretical framework of the estimator has been extended to include its application to non-linear and non-stationary processes. The asymptotic behaviour of the generalized estimator has been analysed in several works, but there seems to be limited empirical studies about the robustness of the estimator on real or synthetic time series. In this paper, we test the robustness of the general Whittle estimator applied to some classes of non-stationary and non-linear long-memory processes. We evaluate the bias and variance of the estimator of the long-memory parameter for different combinations of the correlation parameters and the sample sizes. The numerical results obtained indicate that the performance of the estimator is good, but the sample length necessary to obtain good estimations depends on the type of process and the degree of short and long-term correlation.
机译：Whittle估计器是一种经典统计量，用于测量平稳随机过程的长内存参数。最近，估计器的理论框架已经扩展到包括其在非线性和非平稳过程中的应用。广义估计量的渐近行为已在几篇著作中进行了分析，但是关于估计量在实数或合成时间序列上的鲁棒性的经验研究似乎有限。在本文中，我们测试了适用于某些类型的非平稳和非线性长存储过程的通用Whittle估计器的鲁棒性。对于相关参数和样本量的不同组合，我们评估了长记忆参数估计量的偏差和方差。所获得的数值结果表明，估计器的性能良好，但获得良好估计所需的样本长度取决于过程的类型以及短期和长期相关程度。

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