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Forecasting German GDP using alternative factor models based on large datasets

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dc.creator Schumacher, Christian
dc.date 2005
dc.date.accessioned 2013-10-16T07:05:54Z
dc.date.available 2013-10-16T07:05:54Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/19609
dc.identifier ppn:495867373
dc.identifier RePEc:zbw:bubdp1:4218
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/19609
dc.description This paper discusses the forecasting performance of alternative factor models based on a large panel of quarterly time series for the german economy. One model extracts factors by static principals components analysis, the other is based on dynamic principal components obtained using frequency domain methods. The third model is based on subspace algorithm for state space models. Out-of-sample forecasts show that the prediction errors of the factor models are generally smaller than the errors of simple autoregressive benchmark models. Among the factors models, either the dynamic principal component model or the subspace factor model rank highest in terms of forecast accuracy in most cases. However, neither of the dynamic factor models can provide better forecasts than the static model over all forecast horizons and different specifications of the simulation design. Therefore, the application of the dynamic factor models seems to provide only small forecasting improvements over the static factor model for forecasting German GDP.
dc.language eng
dc.relation Discussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2005,24
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject C51
dc.subject E32
dc.subject C43
dc.subject ddc:330
dc.subject Factor models
dc.subject static and dynamic factors
dc.subject principal components
dc.subject forecasting accuracy
dc.subject Konjunkturprognose
dc.subject Prognoseverfahren
dc.subject Faktorenanalyse
dc.subject Schätzung
dc.subject Deutschland
dc.title Forecasting German GDP using alternative factor models based on large datasets
dc.type doc-type:workingPaper


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