单位根的随机局部和适度偏离及其在单位根检验中的应用

IF 1.2 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Mikihito Nishi, Eiji Kurozumi
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引用次数: 2

摘要

局部到单位和中等偏离规格一直是单位根建模的流行替代方案。本文考虑了另一种偏离单位根的情况,其形式为 c v t / T β,其中 v t 是随机的,而 β 则决定了与单位根的距离。我们将随机偏离分为两类:局部偏离和中度偏离。通过研究假设随机单位根(STUR)过程为替代假设的单位根检验的渐近行为,我们完成了这一分类任务。当 β = 3 / 4 时,出现随机局部到单位模型;在这种情况下,检验统计量的极限分布与单位根零假设下的统计量不同,其渐近幂大于大小。当 1 / 2 ≤ β < 3 / 4 时,会出现适度偏差,在这种情况下,检验统计量会发散。我们还针对 STUR 提出了新的单位根检验方法,其构造基于本文中提出的极限理论。为了评估这些新检验的性能,我们从近似模型中推导出了局部替代下的极限高斯功率包络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Stochastic local and moderate departures from a unit root and its application to unit root testing

Stochastic local and moderate departures from a unit root and its application to unit root testing

Local-to-unity and moderate-deviations specifications have been popular alternatives to unit root modeling. This article considers another kind of departures from a unit root, of the form c v t / T β , where v t is random and β determines the distance from a unit root. We classify the stochastic departures into two types: local and moderate. This classification task is completed by investigating the asymptotic behavior of unit root tests that assume the stochastic unit root (STUR) processes as the alternative hypothesis. The stochastic local-to-unity model arises when β = 3 / 4 ; in this case, the test statistics have limiting distributions different from those under the unit root null, and their asymptotic powers are greater than size. Moderate deviations emerge when 1 / 2 β < 3 / 4 , in which case the test statistics diverge. We also propose new tests for a unit root against an STUR, whose construction is based on the limit theory developed in this article. To evaluate the performance of these new tests, we derive the limiting Gaussian power envelope under the local alternative from an approximate model.

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来源期刊
Journal of Time Series Analysis
Journal of Time Series Analysis 数学-数学跨学科应用
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.
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