Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/53807
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPerevalov, Nikitaen_US
dc.contributor.authorMaier, Philippen_US
dc.date.accessioned2011-12-15T12:56:08Z-
dc.date.available2011-12-15T12:56:08Z-
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/10419/53807-
dc.description.abstractThe good forecasting performance of factor models has been well documented in the literature. While many studies focus on a very limited set of variables (typically GDP and inflation), this study evaluates forecasting performance at disaggregated levels to examine the source of the improved forecasting accuracy, relative to a simple autoregressive model. We use the latest revision of over 100 U.S. time series over the period 1974-2009 (monthly and quarterly data). We employ restrictions derived from national accounting identities to derive jointly consistent forecasts for the different components of U.S. GDP. In line with previous studies, we find that our factor model yields vastly improved forecasts for U.S. GDP, relative to simple autoregressive benchmark models, but we also conclude that the gains in terms of forecasting accuracy differ substantially between GDP components. As a rule of thumb, the largest improvements in terms of forecasting accuracy are found for relatively more volatile series, with the greatest gains coming from improvements of the forecasts for investment and trade. Consumption forecasts, in contrast, perform only marginally better than a simple AR benchmark model. In addition, we show that for most GDP components, an unrestricted, direct forecast outperforms forecasts subject to national accounting identity restrictions. In contrast, GDP itself is best forecasted as the sum of individual forecasts for GDP components, but the improvement over a direct, unconstrained factor forecast is small.en_US
dc.language.isoengen_US
dc.publisher|aBank of Canada |cOttawaen_US
dc.relation.ispartofseries|aBank of Canada Working Paper |x2010,10en_US
dc.subject.jelC50en_US
dc.subject.jelC53en_US
dc.subject.jelE37en_US
dc.subject.jelE47en_US
dc.subject.ddc330en_US
dc.subject.keywordEconometric and statistical methodsen_US
dc.subject.keywordInternational topicsen_US
dc.subject.stwWirtschaftsprognoseen_US
dc.subject.stwSozialprodukten_US
dc.subject.stwInflationsrateen_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwStatistische Methodeen_US
dc.subject.stwAutokorrelationen_US
dc.subject.stwUSAen_US
dc.titleOn the advantages of disaggregated data: Insights from forecasting the US economy in a data-rich environmenten_US
dc.typeWorking Paperen_US
dc.identifier.ppn623481669en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.