Shrinkage Regularization for (Non)Linear Serial Dependence Test

TL;DR

This paper proposes a regularized testing method for detecting linear and nonlinear serial dependence in high-dimensional, non-Gaussian time series, extending existing portmanteau tests to complex data settings.

Contribution

It introduces a novel regularized test that adapts portmanteau methodology for high-dimensional, non-Gaussian time series analysis.

Findings

Effective detection of serial dependence in high-dimensional data

Extension of portmanteau test to non-Gaussian settings

Improved test performance in complex time series

Abstract

This paper introduces a regularized test of the null hypothesis of the absence of linear and nonlinear serial dependence for high-dimensional non-Gaussian time series. Our approach extends the portmanteau test introduced in Jasiak and Neyazi (2023) to the high-dimensional setting.