Bootstrap predictive inference for ARIMA processes

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Show simple item record Pascual, Lorenzo Romo, Juan Ruiz, Esther 2009-07-17T08:53:50Z 2009-07-17T08:53:50Z 2004
dc.identifier.bibliographicCitation Journal of Time Series Analysis, 2004, vol. 25, n. 4, p. 449-465
dc.identifier.issn 1467-9892(online)
dc.identifier.issn 0143-9782(print)
dc.description.abstract In this study, we propose a new bootstrap strategy to obtain prediction intervals for autoregressive integrated moving-average processes. Its main advantage over other bootstrap methods previously proposed for autoregressive integrated processes is that variability due to parameter estimation can be incorporated into prediction intervals without requiring the backward representation of the process. Consequently, the procedure is very flexible and can be extended to processes even if their backward representation is not available. Furthermore, its implementation is very simple. The asymptotic properties of the bootstrap prediction densities are obtained. Extensive finite-sample Monte Carlo experiments are carried out to compare the performance of the proposed strategy vs. alternative procedures. The behaviour of our proposal equals or outperforms the alternatives in most of the cases. Furthermore, our bootstrap strategy is also applied for the first time to obtain the prediction density of processes with moving-average components.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley-Blackwell
dc.rights ©Wiley-Blackwell
dc.subject.other Forecasting
dc.subject.other Non-Gaussian distributions
dc.subject.other Prediction density
dc.subject.other Resampling methods
dc.subject.other Simulation
dc.title Bootstrap predictive inference for ARIMA processes
dc.type article PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Estadística
dc.identifier.doi 10.1111/j.1467-9892.2004.01713.x
dc.rights.accessRights openAccess
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