Show simple item record

dc.contributor.authorMARCELLINO, Massimiliano
dc.contributor.authorSCHUMACHER, Christian
dc.date.accessioned2008-02-13T13:41:07Z
dc.date.available2008-02-13T13:41:07Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/1814/8087
dc.description.abstractThis 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 some- times 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 now- cast 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 ndings 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherEuropean University Institute
dc.relation.ispartofseriesEUI ECOen
dc.relation.ispartofseries2008/16en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectE37en
dc.subjectC53en
dc.subjectnowcastingen
dc.subjectbusiness cycleen
dc.subjectlarge factor modelsen
dc.subjectmixed-frequency dataen
dc.subjectmissing valuesen
dc.subjectMIDASen
dc.titleFactor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDPen
dc.typeWorking Paperen
dc.neeo.contributorMARCELLINO|Massimiliano|aut|EUI70008
dc.neeo.contributorSCHUMACHER|Christian|aut|
eui.subscribe.skiptrue


Files in this item

Icon

This item appears in the following Collection(s)

Show simple item record