多元CARMA过程的因子分解和离散时间表示

Pub Date : 2021-02-23 DOI:10.30757/alea.v19-31
Vicky Fasen-Hartmann, Markus Scholz
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引用次数: 1

摘要

本文证明了在一些温和的假设下,平稳和非平稳的多元连续时间ARMA(MCARMA)过程具有多元复值Ornstein-Uhlenbeck过程的和的表示。证明受益于有理矩阵多项式的性质。结论是对平稳MCARMA过程的自协方差函数的一种替代描述。此外,该表示用于表明,如果存在二阶矩,则离散时间采样的MCARMA(p,q)过程是弱VARMA(p,p−1)过程。该结果补充了Chambers和Thornton[8]中得出的弱VARMA(p,p−1)表示。特别地,它将MCARMA过程的自回归多项式的右溶剂与VARMA过程中的自回归方程的右溶剂联系起来;在一维情况下,正确的溶剂是自回归多项式的零。最后,给出了噪声序列的样本自协方差函数的因子分解,这对统计推断是有用的。
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Factorization and discrete-time representation of multivariate CARMA processes
In this paper we show that stationary and non-stationary multivariate continuous-time ARMA (MCARMA) processes have the representation as a sum of multivariate complexvalued Ornstein-Uhlenbeck processes under some mild assumptions. The proof benefits from properties of rational matrix polynomials. A conclusion is an alternative description of the autocovariance function of a stationary MCARMA process. Moreover, that representation is used to show that the discrete-time sampled MCARMA(p,q) process is a weak VARMA(p, p− 1) process if second moments exist. That result complements the weak VARMA(p, p− 1) representation derived in Chambers and Thornton [8]. In particular, it relates the right solvents of the autoregressive polynomial of the MCARMA process to the right solvents of the autoregressive polynomial of the VARMA process; in the one-dimensional case the right solvents are the zeros of the autoregressive polynomial. Finally, a factorization of the sample autocovariance function of the noise sequence is presented which is useful for statistical inference.
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