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dc.contributor.authorDöhrn, Rolanden_US
dc.date.accessioned2009-01-28T15:48:55Z-
dc.date.available2009-01-28T15:48:55Z-
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/10419/18602-
dc.description.abstractThis paper addresses the question whether forecasters could have been able to produce better forecasts by using the available information more efficiently (informational efficiency of forecast). It is tested whether forecast errors covariate with indicators such as survey results, monetary data, business cycle indicators, or financial data. Because of the short sampling period and data problems, a non parametric ranked sign test is applied. The analysis is carried out for GDP and its main components. The study differentiates between two types of errors: Type I error occurs when forecasters neglect the information provided by an indicator.As type II error a situation is labelled in which forecasters have given too much weight to an indicator. In a number of cases forecast errors and the indicators are correlated, though mostly at a rather low level of significance. In most cases type I errors have been found. Additional tests reveal that there is little evidence of institution specific as well as forecast horizon specific effects. In many cases, co-variations found for GDP are not refected in one of the expenditure side components et vice versa.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseriesRWI Discussion Papers 51en_US
dc.subject.jelC42en_US
dc.subject.jelE37en_US
dc.subject.jelC53en_US
dc.subject.ddc330en_US
dc.subject.keywordShort term forecasten_US
dc.subject.keywordForecast evaluationen_US
dc.subject.keywordinformational efficiencyen_US
dc.titleImproving business cycle forecasts' accuracy : what can we learn from past errors?en_US
dc.typeWorking Paperen_US
dc.identifier.ppn519880587en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:RWI Discussion Papers
Publikationen von Forscherinnen und Forschern des RWI

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