Bootstrap prediction intervals in state space models

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dc.contributor.author Rodríguez, Alejandro
dc.contributor.author Ruiz Ortega, Esther
dc.date.accessioned 2012-10-24T09:25:17Z
dc.date.available 2012-10-24T09:25:17Z
dc.date.issued 2009
dc.identifier.bibliographicCitation Journal of Time Series Analysis, 2009, v. 30, n. 2., pp.167-178
dc.identifier.issn 0143-9782
dc.identifier.uri http://hdl.handle.net/10016/15746
dc.description.abstract Prediction intervals in state space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, with the true parameters substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty caused by parameter estimation. Second, the Gaussianity of future innovations assumption may be inaccurate. To overcome these drawbacks, Wall and Stoffer [Journal of Time Series Analysis (2002) Vol. 23, pp. 733 751] propose a bootstrap procedure for evaluating conditional forecast errors that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. In this article, we propose a bootstrap procedure for constructing prediction intervals directly for the observations, which does not need the backward representation of the model. Consequently, its application is much simpler, without losing the good behaviour of bootstrap prediction intervals. We study its finite sample properties and compare them with those of the standard and the Wall and Stoffer procedures for the local level model. Finally, we illustrate the results by implementing the new procedure to obtain prediction intervals for future values of a real time series.
dc.description.sponsorship Financial support from Project SEJ2006-03919 by the Spanish Government is gratefully acknowledged
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley-Blackwell
dc.rights © 2009 Blackwell Publishing Ltd.
dc.subject.other Backward representation
dc.subject.other Kalman filter
dc.subject.other Local level model
dc.subject.other Unobserved components
dc.title Bootstrap prediction intervals in state space models
dc.type article
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1111/j.1467-9892.2008.00604.x
dc.subject.eciencia Estadística
dc.identifier.doi 10.1111/j.1467-9892.2008.00604.x
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 167
dc.identifier.publicationissue 2
dc.identifier.publicationlastpage 178
dc.identifier.publicationtitle Journal of Time Series Analysis
dc.identifier.publicationvolume 30
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