{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T04:26:53Z","timestamp":1791606413643,"version":"4.3.4"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T00:00:00Z","timestamp":1630713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,18]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>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 $\\min \\lbrace N,T\\rbrace \\rightarrow \\infty$, 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.<\/jats:p>","DOI":"10.1093\/ectj\/utab028","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T15:58:07Z","timestamp":1630943887000},"page":"362-383","source":"Crossref","is-referenced-by-count":13,"title":["Detecting common breaks in the means of high dimensional cross-dependent panels"],"prefix":"10.1093","volume":"25","author":[{"given":"Lajos","family":"Horv\u00e1th","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of Utah, Salt Lake City, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenya","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Finance, Renmin University of China, Beijing, China. China Financial Policy Research Center, Renmin University of China, Beijing, China. CERGAM, Aix-Marseille University, 13090 Aix-en-Provence Cedex 02, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Rice","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Essex Business School, University of Essex, Colchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,9,4]]},"reference":[{"key":"2022052008321955300_bib1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511810800","volume-title":"Matrix Algebra","author":"Abadir","year":"2005"},{"key":"2022052008321955300_bib2","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1080\/07474938.2018.1454378","article-title":"Structural breaks in panel data: large number of panels and short length 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