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dc.contributor.authorPollock, D. S. G
dc.identifier.citationPollock, D. S. G.; "Biquadratic functions: stationary and invertibility in estimated time-series models". Qüestiió. 1989, vol. 13, núm. 1-3
dc.identifier.issn0210-8054 (versió paper)
dc.description.abstractIt is important that the estimates of the parameters of an autoregressive moving-average (ARMA) model should satisfy the conditions of stationarity and invertibility. It can be shown that the unconditional maximum-likelihood estimates are bound to fill these conditions regardless of the size of the sample from which they are derived; and, in some quarters, it has been argued that they should be used in preference to any other estimates when the size of he sample is small. However, the maximum-likelihood estimates are difficult to obtain; and, in practice, estimates are usually derived from a least-squares criterion. In this paper we show that, if an appropriate form of least-squares criterion is adopted, then we can likewise guarantee that the conditions of stationarity and invertibility will be fulfilled. We also re-examine several of the alternative procedures for estimating ARMA models to see whether the criterion functions from which they are derived have the appropriate form.
dc.format.extent18 p.
dc.publisherUniversitat Politècnica de Catalunya. Centre de Càlcul
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Spain
dc.subject.otherARMA models
dc.subject.otherLeast-Squares Estimation
dc.titleBiquadratic functions: stationary and invertibility in estimated time-series models
dc.subject.lemacEquacions en derivades parcials
dc.subject.lemacProblemes de valor inicial
dc.subject.lemacProblemes de contorn
dc.subject.amsClassificació AMS::62 Statistics::62M Inference from stochastic processes
dc.rights.accessOpen Access

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Attribution-NonCommercial-NoDerivs 2.5 Spain
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