MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area
Vladimir Kuzin,
Massimiliano Marcellino and
Christian Schumacher
International Journal of Forecasting, 2011, vol. 27, issue 2, 529-542
Abstract:
This 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.
Keywords: Nowcasting; Mixed-frequency data; Mixed-frequency VAR; MIDAS (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (201)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207010000427
Full text for ScienceDirect subscribers only
Related works:
Journal Article: MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area (2011) 
Working Paper: MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area (2009) 
Working Paper: MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area (2009) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y:2011:i:2:p:529-542
DOI: 10.1016/j.ijforecast.2010.02.006
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().