{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T13:23:51Z","timestamp":1791638631928,"version":"4.3.4"},"reference-count":38,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T00:00:00Z","timestamp":1478217600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["www.cambridge.org"],"crossmark-restriction":true},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:p>For a bivariate time series ((<jats:italic>X<\/jats:italic><jats:sub><jats:italic>i<\/jats:italic><\/jats:sub>,<jats:italic>Y<\/jats:italic><jats:sub><jats:italic>i<\/jats:italic><\/jats:sub>))<jats:sub><jats:italic>i<\/jats:italic>=1,...,<jats:italic>n<\/jats:italic><\/jats:sub>, we want to detect whether the correlation between<jats:italic>X<\/jats:italic><jats:sub><jats:italic>i<\/jats:italic><\/jats:sub>and<jats:italic>Y<\/jats:italic><jats:sub><jats:italic>i<\/jats:italic><\/jats:sub>stays constant for all<jats:italic>i<\/jats:italic>= 1,...<jats:italic>n<\/jats:italic>. We propose a nonparametric change-point test statistic based on Kendall\u2019s tau. The asymptotic distribution under the null hypothesis of no change follows from a new<jats:italic>U<\/jats:italic>-statistic invariance principle for dependent processes. Assuming a single change-point, we show that the location of the change-point is consistently estimated. Kendall\u2019s tau possesses a high efficiency at the normal distribution, as compared to the normal maximum likelihood estimator, Pearson\u2019s moment correlation. Contrary to Pearson\u2019s correlation coefficient, it shows no loss in efficiency at heavy-tailed distributions, and is therefore particularly suited for financial data, where heavy tails are common. We assume the data ((<jats:italic>X<\/jats:italic><jats:sub><jats:italic>i<\/jats:italic><\/jats:sub>,<jats:italic>Y<\/jats:italic><jats:sub><jats:italic>i<\/jats:italic><\/jats:sub>))<jats:sub><jats:italic>i<\/jats:italic>=1,...,<jats:italic>n<\/jats:italic><\/jats:sub>to be stationary and<jats:italic>P<\/jats:italic>-near epoch dependent on an absolutely regular process. The<jats:italic>P<\/jats:italic>-near epoch dependence condition constitutes a generalization of the usually considered<jats:italic>L<\/jats:italic><jats:sub><jats:italic>p<\/jats:italic><\/jats:sub>-near epoch dependence allowing for arbitrarily heavy-tailed data. We investigate the test numerically, compare it to previous proposals, and illustrate its application with two real-life data examples.<\/jats:p>","DOI":"10.1017\/s026646661600044x","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T07:26:44Z","timestamp":1478244404000},"page":"1352-1386","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":38,"title":["TESTING FOR CHANGES IN KENDALL\u2019S 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