巴拿赫空间上整合时间序列的格兰杰-约翰森表示定理

IF 1.2 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Time Series Analysis Pub Date : 2024-08-18 DOI:10.1111/jtsa.12766
Phil Howlett, Brendan K. Beare, Massimo Franchi, John Boland, Konstantin Avrachenkov
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引用次数: 0

摘要

我们证明了巴拿赫空间上有限阶或无限阶积分自回归时间序列的扩展格兰杰-约翰森表示定理(GJRT)。我们仅假定序列的自回归多项式的解析式在单位圆上和单位圆内是解析的,除非在统一处有一个孤立的奇点。如果奇点是有限阶的极点,则时间序列的积分阶数相同。如果奇点是一个本质奇点,则时间序列的积分阶数为无穷大。当不存在确定的强迫时,时间序列在每个时间点的值是一个几乎肯定收敛的随机趋势、一个取决于初始条件的确定项和一个包含在先前观测值中的有限白噪声项的总和。这就是扩展的 GJRT。在每种情况下,原始序列都是互补子空间上两个独立自回归时间序列的总和--一个是与原始序列同阶积分的奇异成分,另一个是未积分的规则成分。扩展的 GJRT 适用于所有积分自回归过程,与空间维度、系统中随机趋势和协整关系的数量以及积分阶数无关。
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The Granger–Johansen representation theorem for integrated time series on Banach space
We prove an extended Granger–Johansen representation theorem (GJRT) for finite‐ or infinite‐order integrated autoregressive time series on Banach space. We assume only that the resolvent of the autoregressive polynomial for the series is analytic on and inside the unit circle except for an isolated singularity at unity. If the singularity is a pole of finite order the time series is integrated of the same order. If the singularity is an essential singularity the time series is integrated of order infinity. When there is no deterministic forcing the value of the series at each time is the sum of an almost surely convergent stochastic trend, a deterministic term depending on the initial conditions and a finite sum of embedded white noise terms in the prior observations. This is the extended GJRT. In each case the original series is the sum of two separate autoregressive time series on complementary subspaces – a singular component which is integrated of the same order as the original series and a regular component which is not integrated. The extended GJRT applies to all integrated autoregressive processes irrespective of the spatial dimension, the number of stochastic trends and cointegrating relations in the system and the order of integration.
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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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