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Forecasting time series with sieve bootstrap

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorAlonso Fernández, Andrés Modesto
dc.contributor.authorPeña, Daniel
dc.contributor.authorRomo, Juan
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2010-12-21T16:17:07Z
dc.date.available2010-12-21T16:17:07Z
dc.date.issued2000-02
dc.description.abstractIn this paper we consider bootstrap methods for constructing nonparametric prediction intervals for a general class of linear processes. Our approach uses the sieve bootstrap procedure of Biihlmann (1997) based on residual resampling from an autoregressive approximation to the given process. We show that the sieve bootstrap provides consistent estimators of the conditional distribution of future values given the observed data, assuming that the order of the autoregressive approximation increases with the sample size at a suitable rate and some restrictions about polynomial decay of the coefficients ~ j t:o of the process MA(oo) representation. We present a Monte Carlo study comparing the finite sample properties of the sieve bootstrap with those of alternative methods. Finally, we illustrate the performance of the proposed method with real data examples.
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/9858
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries00-07
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherSieve boots trap
dc.subject.otherPrediction intervals
dc.subject.otherTime series
dc.subject.otherLinear processes
dc.titleForecasting time series with sieve bootstrap
dc.typeworking paper*
dspace.entity.typePublication
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