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Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP

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dc.creator Marcellino, Massimiliano
dc.creator Schumacher, Christian
dc.date 2007
dc.date.accessioned 2013-10-16T07:06:26Z
dc.date.available 2013-10-16T07:06:26Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/19711
dc.identifier ppn:558626815
dc.identifier RePEc:zbw:bubdp1:7034
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/19711
dc.description This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the 'ragged edge' of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the 'nowcast', using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data.
dc.language eng
dc.relation Discussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2007,34
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject E37
dc.subject C53
dc.subject ddc:330
dc.subject MIDAS
dc.subject large factor models
dc.subject nowcasting
dc.subject mixed-frequency data
dc.subject missing values
dc.subject Konjunkturprognose
dc.subject Sozialprodukt
dc.subject Prognoseverfahren
dc.subject Faktorenanalyse
dc.subject Deutschland
dc.subject Kurzfristprognose
dc.title Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP
dc.type doc-type:workingPaper


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