{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T01:43:59Z","timestamp":1700012639017},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2002,5,15]],"date-time":"2002-05-15T00:00:00Z","timestamp":1021420800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2002,6]]},"abstract":"<jats:p>We frequently observe that one of the aims of time series analysts \nis to predict future values of the data. For weakly dependent \ndata, when the model is known up to a finite set of parameters, \nits statistical properties are well documented and exhaustively \nexamined. However, if the model was misspecified, the predictors \nwould no longer be correct. Motivated by this observation and \nbecause of the interest in obtaining adequate and reliable \npredictors, Bhansali (1974, <jats:italic>Journal of the Royal Statistical \nSociety, Series B<\/jats:italic> 36, 61\u201373) examined the properties \nof a nonparametric predictor based on the canonical factorization \nof the spectral density function given in Whittle (1963, \n<jats:italic>Prediction and Regulation by Linear Least Squares<\/jats:italic>) \nand known as FLES.<\/jats:p><jats:p>However, the preceding work does not cover the so-called strongly \ndependent data. Because of the interest in this type of processes, \none of our objectives in this paper is to examine the properties \nof the FLES for these processes. In addition, we illustrate \nhow the FLES can be adapted to recover the signal of a strongly \ndependent process, showing its consistency. The proposed method \nis semiparametric in the sense that, in contrast to other methods, \nwe do not need to assume any particular model for the noise \nexcept that it is weakly dependent.<\/jats:p>","DOI":"10.1017\/s0266466602183022","type":"journal-article","created":{"date-parts":[[2002,12,11]],"date-time":"2002-12-11T22:13:26Z","timestamp":1039644806000},"page":"584-624","source":"Crossref","is-referenced-by-count":13,"title":["PREDICTION AND SIGNAL EXTRACTION OF STRONGLY DEPENDENT PROCESSES \nIN THE FREQUENCY DOMAIN"],"prefix":"10.1017","volume":"18","author":[{"given":"J.","family":"Hidalgo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y.","family":"Yajima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2002,5,15]]},"container-title":["Econometric Theory"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0266466602183022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T19:50:56Z","timestamp":1554407456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0266466602183022\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002,5,15]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2002,6]]}},"alternative-id":["S0266466602183022"],"URL":"https:\/\/doi.org\/10.1017\/s0266466602183022","relation":{},"ISSN":["0266-4666","1469-4360"],"issn-type":[{"value":"0266-4666","type":"print"},{"value":"1469-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2002,5,15]]}}}