dc.creatorHotta, LK
dc.creatorPereira, PLV
dc.creatorOta, R
dc.date2004
dc.dateDEC
dc.date2014-11-17T17:07:47Z
dc.date2015-11-26T17:40:48Z
dc.date2014-11-17T17:07:47Z
dc.date2015-11-26T17:40:48Z
dc.date.accessioned2018-03-29T00:22:31Z
dc.date.available2018-03-29T00:22:31Z
dc.identifierTest. Sociedad Estadistica Investigacion Operativa, v. 13, n. 2, n. 371, n. 402, 2004.
dc.identifier1133-0686
dc.identifierWOS:000225996400005
dc.identifier10.1007/BF02595778
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/64384
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/64384
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/64384
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1286828
dc.descriptionEconomic time series are of two types: stock and flows, and may be available at different levels of aggregation (for instance, monthly or quarterly). The economist., in many situations, is interested in forecasting the aggregated observations. The forecast function, in this case, can be based either on the disaggregated series or the aggregated series. The forecasts based on the disaggregated data are at least as efficient, in terms of mean squared forecast errors, as the forecasts based on temporally aggregated observations when the data generating process (DGP) is a known ARIMA process. However, the effect of outliers on both forecast functions is not known. In this paper, we consider the effect of additive and innovation outliers on forecasting aggregated values based on aggregated and disaggregated models when the DGP is a known ARIMA process and the presence of the outliers is ignored. Results when the model is not known and tests applied for the detection of outliers are derived through simulation.
dc.description13
dc.description2
dc.description371
dc.description402
dc.languageen
dc.publisherSociedad Estadistica Investigacion Operativa
dc.publisherMadrid
dc.publisherEspanha
dc.relationTest
dc.relationTest
dc.rightsfechado
dc.sourceWeb of Science
dc.subjectadditive outliers
dc.subjectinnovation outliers
dc.subjectforecasting
dc.subjecttemporal aggregation
dc.subjectTime-series Models
dc.subjectAdditive Outliers
dc.subjectParameters
dc.titleEffect of outliers on forecasting temporally aggregated flow variables
dc.typeArtículos de revistas


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