Change Point Estimation in Panel Data with Temporal and Cross-sectional Dependence

Abstract

We study the problem of detecting a common change point in large panel data based on a mean shift model, wherein the errors exhibit both temporal and cross-sectional dependence. A least squares based procedure is used to estimate the location of the change point. Further, we establish the convergence rate and obtain the asymptotic distribution of the least squares estimator. The form of the distribution is determined by the behavior of the norm difference of the means before and after the change point. Since the behavior of this norm difference is, a priori, unknown to the practitioner, we also develop a novel data driven adaptive procedure that provides valid confidence intervals for the common change point, without requiring any such knowledge. Numerical work based on synthetic data illustrates the performance of the estimator in finite samples under different settings of temporal and cross-sectional dependence, sample size and number of panels. Finally, we examine an application to financial stock data and discuss the identified change points.

57 pages, 1 figure, 11 tables

References in corpus (4)

• Change-point detection in panel data via double CUSUM statistic
• Simultaneous multiple change-point and factor analysis for high-dimensional time series
• Large sample behaviour of high dimensional autocovariance matrices
• Common change point estimation in panel data from the least squares and maximum likelihood viewpoints

Cited by in corpus (4)

• Inference on the Change Point for High Dimensional Dynamic Graphical Models
• Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees
• Inference on the change point in high dimensional time series models via plug in least squares
• Inference on the change point with the jump size near the boundary of the region of detectability in high dimensional time series models