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dc.contributor.authorKUZIN, Vladimir
dc.contributor.authorMARCELLINO, Massimiliano
dc.contributor.authorSCHUMACHER, Christian
dc.date.accessioned2012-01-18T09:32:17Z
dc.date.available2012-01-18T09:32:17Z
dc.date.issued2011
dc.identifier.citationInternational Journal of Forecasting, 2011, 27, 2, 529-542en
dc.identifier.issn0169-2070
dc.identifier.urihttp://hdl.handle.net/1814/19955
dc.description.abstractThis paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model specification in the presence of mixed-frequency data, e.g. monthly and quarterly series. MIDAS leads to parsimonious models which are based on exponential lag polynomials for the coefficients, whereas MF-VAR does not restrict the dynamics and can therefore suffer from the curse of dimensionality. However, if the restrictions imposed by MIDAS are too stringent, the MF-VAR can perform better. Hence, it is difficult to rank MIDAS and MF-VAR a priori, and their relative rankings are better evaluated empirically. In this paper, we compare their performances in a case which is relevant for policy making, namely nowcasting and forecasting quarterly GDP growth in the euro area on a monthly basis, using a set of about 20 monthly indicators. It turns out that the two approaches are more complements than substitutes, since MIDAS tends to perform better for horizons up to four to five months, whereas MF-VAR performs better for longer horizons, up to nine months.en
dc.language.isoenen
dc.relation.ispartofInternational Journal of Forecastingen
dc.relation.isversionofhttp://hdl.handle.net/1814/12382
dc.titleMIDAS vs. Mixed-Frequency VAR: Nowcasting GDP in the Euro Areaen
dc.typeArticleen
dc.identifier.doi10.1016/j.ijforecast.2010.02.006
dc.identifier.volume27en
eui.subscribe.skiptrue
dc.description.versionPublished version of EUI ECO WP 2009/32en


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