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dc.contributor.authorClements, Michael P.en_US
dc.contributor.authorGalvão, Ana Beatrizen_US
dc.contributor.authorMarcellino, Massimilianoen_US
dc.date.accessioned2012-09-20T13:00:10Z-
dc.date.available2012-09-20T13:00:10Z-
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/10419/62843-
dc.description.abstractMany macroeconomic series such as US real output growth are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS approach is compared to other ways of making use of monthly data to predict quarterly output growth. The MIDAS specification used in the comparison employs a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way of exploiting monthly data compared to alternative methods. We also exploit the best method to use the monthly vintages of the indicators for real-time forecasting.en_US
dc.language.isoengen_US
dc.publisherQueen Mary, Univ. of London, Dep. of Economics Londonen_US
dc.relation.ispartofseriesWorking Paper, Department of Economics, Queen Mary, University of London 616en_US
dc.subject.jelC51en_US
dc.subject.jelC53en_US
dc.subject.ddc330en_US
dc.subject.keywordMixed data frequencyen_US
dc.subject.keywordCoincident indicatorsen_US
dc.subject.keywordReal-time forecastingen_US
dc.subject.keywordUS output growthen_US
dc.subject.stwWirtschaftsprognoseen_US
dc.subject.stwGesamtwirtschaftliche Produktionen_US
dc.subject.stwStatistische Methodeen_US
dc.subject.stwAutokorrelationen_US
dc.subject.stwUSAen_US
dc.titleMacroeconomic forecasting with mixed frequency data: Forecasting US output growthen_US
dc.typeWorking Paperen_US
dc.identifier.ppn548435383en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:Working Paper Series, School of Economics and Finance, Queen Mary, University of London

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