A High Dimensional Wild Bootstrap Max-Test for Detecting the Presence of Significant Predictors

TL;DR

This paper introduces a high-dimensional wild bootstrap max-test for detecting significant predictors in complex, dependent, and non-stationary data, effectively controlling size and detecting weak signals without covariance estimation.

Contribution

It develops a novel bootstrap max-test that handles ultra-high dimensional data with dependence, avoiding covariance estimation and multiple testing corrections.

Findings

The test controls size well in high-dimensional settings.

It performs effectively against weak or sparse signals.

Numerical and empirical results demonstrate its practical utility.

Abstract

We construct a block bootstrap max-test for detecting the presence of significant predictors in a high dimensional setting, allowing for weakly dependent and heterogeneous (possibly non-stationary) data. The number of covariates to be screened may be large $p$ $>>$ $n$, and growing at an exponential rate, provided $\ln (p)$ $=$ $o(n^{a})$ for some $a$ $>$ $0$ that depends on memory decay and the growth of higher moments. We study the problem of correlation screening in a high dimensional marginal regression setting, assuming so-called \textit{physical dependence} in a time series setting. We entirely sidestep covariance matrix estimation and adaptive re-sampling by working with a max-statistic over the many computed parameters. Thus we do not need endogenous selection of the most relevant predictor index yielding non-uniform asymptotics, nor do we need a post-estimation Bonferroni correction. The non-standard limit distribution arising from the maximum of an increasing number of estimators is easily approximated by a multiplier (wild) block bootstrap. The max-test controls for size well, performs well against various deviations from the null, including very slight deviations with a weak or sparse signal. A numerical experiment is performed and an empirical example with the VIX volatility index is provided.