Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP

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dc.contributor.author MARCELLINO, Massimiliano
dc.contributor.author SCHUMACHER, Christian
dc.date.accessioned 2008-02-13T13:41:07Z
dc.date.available 2008-02-13T13:41:07Z
dc.date.issued 2008
dc.identifier.uri http://hdl.handle.net/1814/8087
dc.description.abstract 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 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.language.iso en en
dc.publisher European University Institute
dc.relation.ispartofseries EUI ECO en
dc.relation.ispartofseries 2008/16 en
dc.subject E37 en
dc.subject C53 en
dc.subject nowcasting en
dc.subject business cycle en
dc.subject large factor models en
dc.subject mixed-frequency data en
dc.subject missing values en
dc.subject MIDAS en
dc.title Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP en
dc.type Working Paper en
dc.neeo.contributor MARCELLINO|Massimiliano|aut|EUI70008
dc.neeo.contributor SCHUMACHER|Christian|aut|
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