Publication:
An Asymptotically Pivotal Transform of the Residuals Sample Autocorrelations With Application to Model Checking

Simple item page
dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorDelgado, Miguel A.
dc.contributor.authorVelasco, Carlos
dc.date.accessioned2012-07-30T16:37:23Z
dc.date.available2012-07-30T16:37:23Z
dc.date.issued2012-01-24
dc.description.abstractWe propose an asymptotically distribution-free transform of the sample autocorrelations of residuals in general parametric time series models, possibly nonlinear in variables. The residuals autocorrelation function is the basic model checking tool in time series analysis, but it is not useful when its distribution is incorrectly approximated because the effects of parameter estimation and/or higher-order serial dependence have not been taken into account. The limiting distribution of the residuals sample autocorrelations may be difficult to derive, particularly when the underlying innovations are uncorrelated but not independent. In contrast, our proposal is easily implemented in fairly general contexts and the resulting transformed sample autocorrelations are asymptotically distributed as independent standard normals when innovations are uncorrelated, providing an useful and intuitive device for time series model checking in the presence of estimated parameters. We also discuss in detail alternatives to the classical Box–Pierce test, showing that our transform entails no efficiency loss under Gaussianity in the direction of MA and AR departures from the white noise hypothesis, as well as alternatives to Bartlett’s Tp-process test. The finite-sample performance of the procedures is examined in the context of a Monte Carlo experiment for the new goodness-of-fit tests discussed in the article. The proposed methodology is applied to modeling the autocovariance structure of the well-known chemical process temperature reading data already used for the illustration of other statistical procedures. Additional technical details are included in a supplemental material online.
dc.description.sponsorshipResearch funded by Spanish Plan Nacional de I+D+i grant number SEJ2007-62908/ECON, Consolider-Ingenio 2010, and Excelecon-Comunidad de Madrid. We are grateful to the Editor, Associate Editor, and two referees for helpful comments.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationJournal of the American Statistical Association, 2011. v. 106, n. 495, pp. 946-958
dc.identifier.doi10.1198/jasa.2011.tm10226
dc.identifier.issn0162-1459
dc.identifier.publicationfirstpage946
dc.identifier.publicationissue495
dc.identifier.publicationlastpage958
dc.identifier.publicationtitleJournal of the American Statistical Association
dc.identifier.publicationvolume106
dc.identifier.urihttps://hdl.handle.net/10016/15032
dc.language.isoeng
dc.publisherAmerican Statistical Association
dc.relation.publisherversionhttp://dx.doi.org/10.1198/jasa.2011.tm10226
dc.rights© 2011 American Statistical Association, Journal of the American Statistical Association
dc.rights.accessRightsopen access
dc.subject.ecienciaEconomía
dc.subject.otherHigher-order serial dependence
dc.subject.otherLocal alternatives
dc.subject.otherLong memory
dc.subject.otherModel checking
dc.subject.otherNonlinear in variables models
dc.subject.otherRecursive residuals
dc.titleAn Asymptotically Pivotal Transform of the Residuals Sample Autocorrelations With Application to Model Checking
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
asymptotically_delgado_JASA_2011_ps.pdf
Size:
844.5 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
DE - Artículos de Revistas
Economists Online