dc.contributor.author | Arteche González, Jesús María  | |
dc.date.accessioned | 2024-09-04T14:14:17Z | |
dc.date.available | 2024-09-04T14:14:17Z | |
dc.date.issued | 2020-03-05 | |
dc.identifier.citation | Econometric Theory 36(6) : 1064-1098 (2020) | es_ES |
dc.identifier.issn | 0266-4666 | |
dc.identifier.uri | http://hdl.handle.net/10810/69409 | |
dc.description.abstract | A 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.sponsorship | Research 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.iso | eng | es_ES |
dc.publisher | Cambridge University Press | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIN/ECO2016-76884-P | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | long memory | es_ES |
dc.subject | seasonality | es_ES |
dc.subject | whittle estimation | es_ES |
dc.subject | non-stationarity | es_ES |
dc.subject | non-invertibility | es_ES |
dc.title | Exact Local Whittle estimation in long memory time series with multiple poles | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2020 Cambridge University Press | es_ES |
dc.relation.publisherversion | https://doi.org/10.1017/S0266466619000422 | es_ES |
dc.identifier.doi | 10.1017/S0266466619000422 | |
dc.departamentoes | Métodos Cuantitativos | es_ES |
dc.departamentoeu | Metodo Kuantitatiboak | es_ES |