Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/40262 
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dc.contributor.authorHyung, Namwonen
dc.date.accessioned2010-07-27-
dc.date.accessioned2010-09-24T14:29:41Z-
dc.date.available2010-09-24T14:29:41Z-
dc.date.issued1998-
dc.identifier.urihttp://hdl.handle.net/10419/40262-
dc.description.abstractThis paper systematically studies the use of mixed-frequency data sets and suggests that the use of high frequency data in forecasting economic aggregates can improve forecast accuracy. The best way of using this information is to build a single model, for example, an ARMA model with missing observations, that relates data of all frequencies. The implementation of such an approach, however, poses serious practical problems in all but the simplest cases. As a feasible and consistent alternative, we propose a two-stage procedure to obtain pseudo high frequency data and to subsequently use these artificial values as proxies for macroeconomic or financial models. This alternative method yields a sub-optimal forecast in general but avoids the computational problems of a full-blown single model. Our approach differs from classical interpolation since we only use past and current information to get the pseudo series. A proxy, which is constructed by classical interpolation, may fit very well in sample, but it is not useful for out-of-sample forecasts. As applications of linking series generated at different frequencies, we show that the use of monthly proxies of GDP improves the predictability of absolute stock returns and the unemployment rate compared to the use of industrial production as an alternative proxy.en
dc.language.isoengen
dc.publisher|aDeutsche Bank Research |cFrankfurt a. M.en
dc.relation.ispartofseries|aResearch Notes |x99-1en
dc.subject.jelC3en
dc.subject.jelC4en
dc.subject.jelC5en
dc.subject.ddc330en
dc.subject.keywordLinked ARMAen
dc.subject.keywordKalman Filteren
dc.subject.keywordInterpolationen
dc.subject.keywordTemporal Transformationen
dc.subject.keywordHigh Frequency Dataen
dc.subject.keywordVirtual Reality Variableen
dc.subject.stwZeitreihenanalyseen
dc.subject.stwPrognoseverfahrenen
dc.subject.stwÖkonometrieen
dc.subject.stwTheorieen
dc.titleLinking series generated at different frequencies and its applications-
dc.typeWorking Paperen
dc.identifier.ppn821865935en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:dbrrns:991en

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