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首页> 外文期刊>Advances in decision sciences >On the Causality between Multiple Locally Stationary Processes
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On the Causality between Multiple Locally Stationary Processes

机译:关于多个局部平稳过程之间的因果关系

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摘要

When one would like to describe the relations between multivariate time series, the concepts of dependence and causality are of importance. These concepts also appear to be useful when one is describing the properties of an engineering or econometric model. Although the measures of dependence and causality under stationary assumption are well established, empirical studies show that these measures are not constant in time. Recently one of the most important classes of nonstationary processes has been formulated in a rigorous asymptotic framework by Dahlhaus in (1996), (1997), and (2000), called locally stationary processes. Locally stationary processes have time-varying spectral densities whose spectral structures smoothly change in time. Here, we generalize measures of linear dependence and causality to multiple locally stationary processes. We give the measures of linear dependence, linear causality from one series to the other, and instantaneous linear feedback, at timetand frequencyλ.
机译:当想要描述多元时间序列之间的关系时,依存关系和因果关系的概念很重要。当人们描述工程或计量模型的属性时,这些概念似乎也很有用。尽管在固定假设下对依赖关系和因果关系的度量标准已经确立,但经验研究表明,这些度量标准在时间上并不是恒定不变的。最近,Dahlhaus在(1996),(1997)和(2000)的严格渐近框架中提出了最重要的一类非平稳过程,称为局部平稳过程。局部平稳过程具有随时间变化的光谱密度,其光谱结构随时间平滑变化。在这里,我们将线性相关性和因果关系的度量推广到多个局部平稳过程。我们给出了在时间和频率λ处的线性相关性,从一个序列到另一个序列的线性因果关系以及瞬时线性反馈的度量。

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