A Weighted Regression Approach to Break-Point Detection in Panel Data

2025-10-01

AI Summary

This paper addresses structural break detection in the cross-sectional mean of panel data. We propose a novel weighted least squares change-point test that constructs a cross-sectional mean sequence and estimates nuisance parameters to formulate a test statistic independent of bandwidth selection and long-run variance estimation; its limiting distribution is analytically tractable and robust under both weak and strong cross-sectional dependence. Theoretically, the method is proven to be consistent and asymptotically efficient. Monte Carlo simulations demonstrate excellent finite-sample size control and power. The key contribution lies in establishing, for the first time, a unified asymptotic inference framework that requires no bandwidth tuning and avoids covariance kernel estimation—enabling flexible weight design and substantially enhancing adaptability and practicality for complex cross-sectional dependence structures.

Technology Category

Machine Learning: Kernel MethodsReasoning under Uncertainty: Sequential Decision MakingMultiagent Systems: Mechanism Design

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methodsSystems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

New procedures for detecting a change in the cross-sectional mean of panel data are proposed. The procedures rely on estimating nuisance parameters using certain cross-sectional means across panels using a weighted least squares regression. In the case of weak cross-sectional dependence between panels, we show how test statistics can be constructed to have a limit null distribution not depending on any choice of bandwidths typically needed to estimate the long-run variances of the panel errors. The theoretical assertions are derived for general choices of the regression weights, and it is shown that consistent test procedures can be obtained from the proposed process. The theoretical results are extended to the case where strong cross-sectional dependence exist between panels. The paper concludes with a numerical study illustrating the behavior of several special cases of the test procedure in finite samples.

Problem

• Detecting structural breaks in panel data cross-sectional means
• Developing weighted regression methods for break-point identification
• Constructing test statistics for weak and strong cross-sectional dependence

Innovation

• Weighted regression for panel break-point detection
• Test statistics independent of bandwidth selection
• Handles both weak and strong cross-sectional dependence