High Dimensional Correlation Matrices: CLT and Its Applications

TL;DR

This paper establishes a new CLT for spectral statistics of high-dimensional correlation matrices, enabling improved independence testing and factor analysis in large datasets, with demonstrated practical effectiveness.

Contribution

It introduces a novel CLT for linear spectral statistics of high-dimensional correlation matrices, advancing theoretical understanding and practical testing methods.

Findings

New CLT for spectral statistics in high dimensions

Effective independence and factor loadings tests

Empirical validation on household income data

Abstract

Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT) for a linear spectral statistic (LSS) of high dimensional sample correlation matrices for the case where the dimension p and the sample size $n$ are comparable. This result is of independent interest in large dimensional random matrix theory. Meanwhile, we apply the linear spectral statistic to an independence test for $p$ random variables, and then an equivalence test for p factor loadings and $n$ factors in a factor model. The finite sample performance of the proposed test shows its applicability and effectiveness in practice. An empirical application to test the independence of household incomes from different cities in China is also conducted.