{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T03:36:32Z","timestamp":1791171392377,"version":"4.1.0"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2002,3,6]],"date-time":"2002-03-06T00:00:00Z","timestamp":1015372800000},"content-version":"unspecified","delay-in-days":33,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2002,2]]},"abstract":"<jats:p>In this paper we study nonparametric estimation of regression \nquantiles for time series data by inverting a weighted \nNadaraya\u2013Watson (WNW) estimator of conditional distribution \nfunction, which was first used by Hall, Wolff, and Yao (1999, \n<jats:italic>Journal of the American Statistical Association<\/jats:italic> 94, \n154\u2013163). First, under some regularity conditions, we \nestablish the asymptotic normality and weak consistency of the \nWNW conditional distribution estimator for \u03b1-mixing time \nseries at both boundary and interior points, and we show that \nthe WNW conditional distribution estimator not only preserves \nthe bias, variance, and, more important, automatic good boundary \nbehavior properties of local linear \u201cdouble-kernel\u201d \nestimators introduced by Yu and Jones (1998, <jats:italic>Journal of \nthe American Statistical Association<\/jats:italic> 93, 228\u2013237), \nbut also has the additional advantage of always being a \ndistribution itself. Second, it is shown that under some regularity \nconditions, the WNW conditional quantile estimator is weakly \nconsistent and normally distributed and that it inherits all \ngood properties from the WNW conditional distribution estimator. \nA small simulation study is carried out to illustrate the \nperformance of the estimates, and a real example is also used \nto demonstrate the methodology.<\/jats:p>","DOI":"10.1017\/s0266466602181096","type":"journal-article","created":{"date-parts":[[2002,11,14]],"date-time":"2002-11-14T22:01:41Z","timestamp":1037311301000},"page":"169-192","source":"Crossref","is-referenced-by-count":130,"title":["REGRESSION QUANTILES FOR TIME SERIES"],"prefix":"10.1017","volume":"18","author":[{"given":"Zongwu","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2002,3,6]]},"container-title":["Econometric Theory"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0266466602181096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,6]],"date-time":"2019-04-06T19:47:33Z","timestamp":1554580053000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0266466602181096\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002,2]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2002,2]]}},"alternative-id":["S0266466602181096"],"URL":"https:\/\/doi.org\/10.1017\/s0266466602181096","relation":{},"ISSN":["0266-4666","1469-4360"],"issn-type":[{"value":"0266-4666","type":"print"},{"value":"1469-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2002,2]]}}}