A Max-Correlation White Noise Test for Weakly Dependent Time Series

TL;DR

This paper introduces a bootstrapped white noise test based on maximum correlation for weakly dependent time series, effective with residuals and large lag sets, validated through theoretical proofs and experiments.

Contribution

It develops a new max-correlation white noise test with bootstrap validation applicable to a broad class of weakly dependent processes and residuals from parametric models.

Findings

Test achieves accurate size under null hypotheses

High power against distant serial dependence

Bootstrap validity extends to non-mixing sequences

Abstract

This paper presents a bootstrapped p-value white noise test based on the maximum correlation, for a time series that may be weakly dependent under the null hypothesis. The time series may be prefiltered residuals. The test statistic is a normalized weighted maximum sample correlation, where the maximum lag increases at a rate slower than the sample size. We only require uncorrelatedness under the null hypothesis, along with a moment contraction dependence property that includes mixing and non-mixing sequences. We show Shao's (2011) dependent wild bootstrap is valid for a much larger class of processes than originally considered. It is also valid for residuals from a general class of parametric models as long as the bootstrap is applied to a first order expansion of the sample correlation. We prove the bootstrap validity without exploiting extreme value theory (standard in the literature) or recent Gaussian approximation theory. Finally, we extend Escanciano and Lobato's (2009) automatic maximum lag selection to our setting with an unbounded choice set, and find it works strikingly well in controlled experiments. Our proposed test achieves accurate size under various white noise null hypotheses and high power under various alternative hypotheses including distant serial dependence.