Testing High-dimensional Nonstationary Time Series

Fuente: arXiv
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Main Authors: Liu, Ruihan, Wang, Chen
Format: Preprint
Published: 2023
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author Liu, Ruihan
Wang, Chen
author_facet Liu, Ruihan
Wang, Chen
contents In this article, we first establish the joint central limit theorem (CLT) for the extreme eigenvalues of the sample correlation matrix of high-dimensional random walks with cross-sectional dependence. We further investigate the asymptotic spectral properties of the sample correlation matrix of high-dimensional autoregressive processes. To apply our theoretical results, we propose a novel high-dimensional unit root test and develop a forward sequential test to determine the number of unit roots in high-dimensional time series data. Finally, we conduct an empirical study of the purchasing power parity (PPP) hypothesis in high-dimensional settings.
format Preprint
id arxiv_https___arxiv_org_abs_2308_06126
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Testing High-dimensional Nonstationary Time Series
Liu, Ruihan
Wang, Chen
Methodology
62H15, 60B20, 62H10
In this article, we first establish the joint central limit theorem (CLT) for the extreme eigenvalues of the sample correlation matrix of high-dimensional random walks with cross-sectional dependence. We further investigate the asymptotic spectral properties of the sample correlation matrix of high-dimensional autoregressive processes. To apply our theoretical results, we propose a novel high-dimensional unit root test and develop a forward sequential test to determine the number of unit roots in high-dimensional time series data. Finally, we conduct an empirical study of the purchasing power parity (PPP) hypothesis in high-dimensional settings.
title Testing High-dimensional Nonstationary Time Series
topic Methodology
62H15, 60B20, 62H10
url https://arxiv.org/abs/2308.06126