Publication:
Effects of parameter estimation on prediction densities a bootstrap approach

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPascual, Lorenzo
dc.contributor.authorRomo, Juan
dc.contributor.authorRuiz Ortega, Esther
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2010-01-08T10:32:54Z
dc.date.available2010-01-08T10:32:54Z
dc.date.issued1999-04
dc.description.abstractIn this paper, we study the impact of parameter estimation on prediction densities using a bootstrap strategy to estimate these densities. We focus on seasonal ARlMA processes with possibly non normal innovations. We compare prediction densities obtained using the Box and Jenkins approach with bootstrap densities which may be constructed taking into account parameter estimation variability (PRR) or using parameter estimates as if they were the true parameters (CB). By means of Monte Carlo experiments, we show that the average coverage of the intervals is closer to the nominal value when intervals are constructed incorporating parameter uncertainty. The effects of parameter estimation are particularly important for small sample sizes and when the error distribution is not Gaussian. We also analyze the effect of the estimation method on the shape of prediction densities comparing prediction densities constructed when the parameters are estimated by OLS and by LAD. We show how, when the error distribution is not Gaussian, the average coverage and length of intervals based on LAD estimates are closer to nominal values than those based on OLS estimates. Finally, the performance of the PRR procedure is illustrated with two empirical examples.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/6304
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries99-31-09
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.otherForescating
dc.subject.otherLeast absolute deviations
dc.subject.othernon normal distributions
dc.subject.otherOrdinaty least squares
dc.titleEffects of parameter estimation on prediction densities a bootstrap approach
dc.typeworking paper*
dspace.entity.typePublication
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