Abstract:
This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-
VAR) approaches to model speci cation in the presence of mixed-frequency data, e.g.,
monthly and quarterly series. MIDAS leads to parsimonious models based on exponential
lag polynomials for the coe¢ cients, whereas MF-VAR does not restrict the dynamics and
therefore can su¤er from the curse of dimensionality. But if the restrictions imposed by
MIDAS are too stringent, the MF-VAR can perform better. Hence, it is di¢ cult to rank
MIDAS and MF-VAR a priori, and their relative ranking is better evaluated empirically.
In this paper, we compare their performance in a relevant case for policy making, i.e.,
nowcasting and forecasting quarterly GDP growth in the euro area, on a monthly basis
and using a set of 20 monthly indicators. It turns out that the two approaches are
more complementary than substitutes, since MF-VAR tends to perform better for longer
horizons, whereas MIDAS for shorter horizons.
