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Detecting common breaks in the means of high dimensional cross-dependent panels

Horvath, Lajos, Liu, Zhenya, Rice, Gregory, Zhao, Y. (2022) Detecting common breaks in the means of high dimensional cross-dependent panels. Econometrics Journal, 25 (2). pp. 362-383. ISSN 1368-4221. (doi:10.1093/ectj/utab028) (KAR id:95054)


The problem of detecting change points in the mean of high dimensional panel data with potentially strong cross-sectional dependence is considered. Under the assumption that the cross-sectional dependence is captured by an unknown number of common factors, a new CUSUM-type statistic is proposed. We derive its asymptotic properties under three scenarios depending on to what extent the common factors are asymptotically dominant. With panel data consisting of N cross sectional time series of length T, the asymptotic results hold under the mild assumption that min{N,T}→∞⁠, with an otherwise arbitrary relationship between N and T, allowing the results to apply to most panel data examples. Bootstrap procedures are proposed to approximate the sampling distribution of the test statistics. A Monte Carlo simulation study showed that our test outperforms several other existing tests in finite samples in a number of cases, particularly when N is much larger than T. The practical application of the proposed results are demonstrated with real data applications to detecting and estimating change points in the high dimensional FRED-MD macroeconomic data set.

Item Type: Article
DOI/Identification number: 10.1093/ectj/utab028
Subjects: H Social Sciences
H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Yuqian Zhao
Date Deposited: 17 May 2022 10:30 UTC
Last Modified: 27 Oct 2023 13:16 UTC
Resource URI: (The current URI for this page, for reference purposes)

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