English

Limit theorems for linear processes with tapered innovations and filters

Probability 2021-11-17 v1

Abstract

In the paper we consider the partial sum process k=1[nt]Xk(n)\sum_{k=1}^{[nt]}X_k^{(n)}, where {Xk(n)=j=0aj(n)ξkj(b(n)), k\bz}, n1,\{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 aj(n)=aj\ind[0j\l(n)]a_{j}^{(n)}=a_{j}\ind{[0\le j\le \l(n)]} and heavy-tailed tapered innovations ξj(b(n), j\bz\xi_{j}(b(n), \ j\in \bz. Both tapering parameters b(n)b(n) and \l(n)\l(n) grow to \infty as nn\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 ai, i0a_i, \ i\ge 0 and non-tapered innovations. We consider the case where b(n)b(n) grows relatively slow (soft tapering), and all three cases of growth of \l(n)\l(n) (strong, weak, and moderate tapering). In these cases the limit processes (in the sense of convergence of finite dimensional distributions) are Gaussian.

Cite

@article{arxiv.2111.08321,
  title  = {Limit theorems for linear processes with tapered innovations and filters},
  author = {Vygantas Paulauskas},
  journal= {arXiv preprint arXiv:2111.08321},
  year   = {2021}
}

Comments

23 pages

R2 v1 2026-06-24T07:40:12.883Z