Consistent and powerful CUSUM change-point test for panel data with changes in variance

TL;DR

This paper develops a new CUSUM-based test for detecting variance change-points in panel data with mixing conditions, demonstrating superior performance in simulations and real-world financial data analysis.

Contribution

It introduces a powerful CUSUM test for variance change detection in panel data, with derived limit distributions and validation through simulations and empirical case study.

Findings

The test accurately detects variance change-points in simulated data.

It outperforms existing methods, especially for sparse variance changes.

Applied to financial data, it identified significant variance shifts and economic drivers.

Abstract

This paper investigates change-point of variance in panel data models with time series of $\alpha$-mixing. Based on the cumulative sum (CUSUM) method and the individual differences, we construct a CUSUM test for panel data models to detect variance changes. Under the null hypothesis, we derive the limit distribution of this test, which can be used to detect the change-point of variance. Under the alternative hypothesis, the limit behavior of the CUSUM test is also derived. To validate the performance of the test, we conducted simulation analyses on with Gaussian and Gamma errors. The results demonstrate that this testing method significantly outperforms existing approaches, particularly in detecting sparse variance changes. Finally, we conducted a practical case study using panel data from the Shanghai Shenzhen CSI 300 Index Components. Not only did we successfully identify the change-points of variance, but we also delved deeper into the underlying economic drivers behind these changes.