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dc.contributor.authorWall, Kent D.
dc.contributor.authorStoffer, David S.
dc.date.accessioned2016-02-12T20:46:11Z
dc.date.available2016-02-12T20:46:11Z
dc.date.issued2002
dc.identifier.citationJournal of Time Series Analysis, v. 23, no. 6, 2002, pp. 733-751en_US
dc.identifier.urihttp://hdl.handle.net/10945/47794
dc.description.abstractA bootstrap approach to evaluating conditional forecast errors in ARMA models is presented. The key to this method is the derivation of a reverse-time state space model for generating conditional data sets that capture the salient stochastic properties of the observed data series. We demonstrate the utility of the method using several simulation experiments for the MA(q) and ARMA( p, q) models. Using the state space form, we are able to investigate conditional forecast errors in these models quite easily whereas the existing literature has only addressed conditional forecast error assessment in the pure AR( p) form. Our experiments use short data sets and non-Gaussian, as well as Gaussian, disturbances. The bootstrap is found to provide useful information on error distributions in all cases and serves as a broadly applicable alternative to the asymptotic Gaussian theory.en_US
dc.format.extent19 p.en_US
dc.publisherBlackwell Publishersen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleA state space approach to bootstrapping conditional forecasts in ARMA modelsen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentDefense Resource Management Instituteen_US
dc.subject.authorBootstrapen_US
dc.subject.authorState spaceen_US
dc.subject.authorForecastingen_US
dc.subject.authorPrediction errorsen_US
dc.subject.authorSimulationen_US


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