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dc.contributor.advisorGeneralized inverse
dc.contributor.authorCarrión García, Andrés
dc.identifier.citationCarrión García, Andrés. "Algunas características de los modelos agregados MC-MN de modelos MA." Qüestiió. 1988, vol. 12, núm. 1
dc.identifier.issn0210-8054 (versió paper)
dc.description.abstractSe estudian algunas propiedades de los modelos agregados de mínimos cuadrados y mínima norma de procesos MA. Dichos agregados MC-MN se obtienen mediante una metodología matricial desarrollada por el autor, que es aquí brevemente esbozada. Las características analizadas se refieren a la multiplicatividad de las estructuras componentes del modelo y la invertibilidad del modelo agregado. 1980 M.S.C...... 62M10 (Time series).
dc.description.abstractA matricial approach to the problem of temporal aggregation in Time series with ARIMA structure and the use of the generalized inverse matrix, enables us to solve the question of the change of the model form from the original to the aggregated series. A general solution for the orders and parameter's values is given. In the case of MA models, the exact analytic expression of the aggregated model parameters is found. From these previous results, some interesting consecuences can be derived. Two aspects oí the aggregated model are presented here: * A study of the conditions under wich the aggregate model conserves mul- tiplicativity of structures. * A studdy of the invertibility of an aggregate model of a MA series, related with the invertibility of the original series.
dc.publisherUniversitat Politècnica de Barcelona. Centre de Càlcul
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Spain
dc.subject.otherTime series
dc.subject.otherTemporal aggregation
dc.subject.otherGeneralized Inverse
dc.titleAlgunas características de los modelos agregados MC-MN de modelos MA.
dc.title.alternativeSome Characteristics of the MN-LS aggregated modela of MA pro- cesa.
dc.subject.lemacProcessos estocàstics
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::62 Statistics::62M Inference from stochastic processes
dc.rights.accessOpen Access

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