Bootstrap prediction intervals for power-transformed time series

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dc.contributor.author Pascual, Lorenzo
dc.contributor.author Romo, Juan
dc.contributor.author Ruiz, Esther
dc.date.accessioned 2006-11-08T15:55:51Z
dc.date.available 2006-11-08T15:55:51Z
dc.date.issued 2001-01
dc.identifier.uri http://hdl.handle.net/10016/150
dc.description.abstract In this paper we propose a bootstrap resampling scheme to construct prediction intervals for future values of a variable after a linear ARIMA model has been fitted to a power transformation of it. The advantages over existing methods for computing prediction intervals of power transformed time series are that the proposed bootstrap intervals incorporate the variability due to parameter estimation, and do not rely on distributional assumptions neither on the original variable nor on the transformed one. We show the good behavior of the bootstrap approach versus alternative procedures by means of Monte Carlo experiments. Finally, the procedure is illustrated by analysing three real time series data sets.
dc.format.extent 232471 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 2001-03
dc.title Bootstrap prediction intervals for power-transformed time series
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws010503
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