{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T09:22:30Z","timestamp":1774516950613,"version":"3.50.1"},"reference-count":13,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"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":[],"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We introduce a random-perturbation-based rank estimator of the number of factors of a large-dimensional approximate factor model. An expansion of the rank estimator demonstrates that the random perturbation reduces the biases due to the persistence of the factor series and the dependence between the factor and error series. A central limit theorem for the rank estimator with convergence rate higher than root $n$ gives a new hypothesis-testing procedure for both one-sided and two-sided alternatives. Simulation studies verify the performance of the test.<\/jats:p>","DOI":"10.1093\/biomet\/asz073","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T12:09:53Z","timestamp":1573646993000},"source":"Crossref","is-referenced-by-count":2,"title":["A random-perturbation-based rank estimator of the number of factors"],"prefix":"10.1093","author":[{"given":"Xinbing","family":"Kong","sequence":"first","affiliation":[{"name":"School of Statistics and Mathematics, Nanjing Audit University, 86 West Yushan Road, Nanjing 211815, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"2020021221380156300_B1","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.3982\/ECTA8968","article-title":"Eigenvalue ratio test for the number of factors","volume":"81","author":"Ahn,","year":"2013","journal-title":"Econometrica"},{"key":"2020021221380156300_B2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1111\/1468-0262.00273","article-title":"Determining the number of factors in approximate factor models","volume":"70","author":"Bai,","year":"2002","journal-title":"Econometrica"},{"key":"2020021221380156300_B3","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.2307\/1912275","article-title":"Arbitrage, factor structure, and mean-variance analysis on large asset markets","volume":"51","author":"Chamberlain,","year":"1983","journal-title":"Econometrica"},{"key":"2020021221380156300_B4","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1111\/j.1540-6261.1993.tb04754.x","article-title":"A test for the number of factors in an approximate factor model","volume":"48","author":"Connor,","year":"1993","journal-title":"J. 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