Limit theorems for linear processes with tapered innovations and filters

In the paper we consider the partial sum process $\sum_{k=1}^{[nt]}X_k^{(n)}$, where $\{X_k^{(n)}=\sum_{j=0}^{\infty} a_{j}^{(n)}\xi_{k-j}(b(n)), \ k\in \bz\},\ n\ge 1,$ is a series of linear processes with tapered filter $a_{j}^{(n)}=a_{j}\ind{[0\le j\le \l(n)]}$ and heavy-tailed tapered innovation...

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Bibliographic Details
Main Author Paulauskas, Vygantas
Format Journal Article
LanguageEnglish
Published 16.11.2021
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Summary:In the paper we consider the partial sum process $\sum_{k=1}^{[nt]}X_k^{(n)}$, where $\{X_k^{(n)}=\sum_{j=0}^{\infty} a_{j}^{(n)}\xi_{k-j}(b(n)), \ k\in \bz\},\ n\ge 1,$ is a series of linear processes with tapered filter $a_{j}^{(n)}=a_{j}\ind{[0\le j\le \l(n)]}$ and heavy-tailed tapered innovations $\xi_{j}(b(n), \ j\in \bz$. Both tapering parameters $b(n)$ and $\l(n)$ grow to $\infty$ as $n\to \infty$. The limit behavior of the partial sum process depends on the growth of these two tapering parameters and dependence properties of a linear process with non-tapered filter $a_i, \ i\ge 0$ and non-tapered innovations. We consider the case where $b(n)$ grows relatively slow (soft tapering), and all three cases of growth of $\l(n)$ (strong, weak, and moderate tapering). In these cases the limit processes (in the sense of convergence of finite dimensional distributions) are Gaussian.
DOI:10.48550/arxiv.2111.08321