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dc.contributor.authorArteche González, Jesús María ORCID
dc.date.accessioned2024-09-04T14:14:17Z
dc.date.available2024-09-04T14:14:17Z
dc.date.issued2020-03-05
dc.identifier.citationEconometric Theory 36(6) : 1064-1098 (2020)es_ES
dc.identifier.issn0266-4666
dc.identifier.urihttp://hdl.handle.net/10810/69409
dc.description.abstractA generalization of the Exact Local Whittle estimator in Shimotsu and Phillips (2005) is proposed for jointly estimating all the memory parameters in general long memory time series that possibly display standard, seasonal and/or other cyclical strong persistence. Consistency and asymptotic normality are proven for stationary, non-stationary and noninvertible series, permitting straightforward standard inference of interesting hypotheses such as the existence of unit roots and equality of memory parameters at some or all seasonal frequencies, which can be used as a prior test for the application of seasonal differencing filters. The effects of unknown deterministic terms are also discussed. Finally, the finite sample performance is analysed in an extensive Monte Carlo exercise and an application to an U.S. Industrial Production index.es_ES
dc.description.sponsorshipResearch was supported by the Spanish Ministry of Science and Innovation and ERDF grant ECO2016-76884-P, and UPV/EHU Econometrics Research Group, Basque Government grant IT1359-19.es_ES
dc.language.isoenges_ES
dc.publisherCambridge University Presses_ES
dc.relationinfo:eu-repo/grantAgreement/MCIN/ECO2016-76884-Pes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectlong memoryes_ES
dc.subjectseasonalityes_ES
dc.subjectwhittle estimationes_ES
dc.subjectnon-stationarityes_ES
dc.subjectnon-invertibilityes_ES
dc.titleExact Local Whittle estimation in long memory time series with multiple poleses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2020 Cambridge University Presses_ES
dc.relation.publisherversionhttps://doi.org/10.1017/S0266466619000422es_ES
dc.identifier.doi10.1017/S0266466619000422
dc.departamentoesMétodos Cuantitativoses_ES
dc.departamentoeuMetodo Kuantitatiboakes_ES


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