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 exponentiallag polynomials for the coe¢ cients, whereas MF-VAR does not restrict the dynamics andtherefore can su¤er from the curse of dimensionality. But if the restrictions imposed byMIDAS are too stringent, the MF-VAR can perform better. Hence, it is di¢ cult to rankMIDAS 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 basisand using a set of 20 monthly indicators. It turns out that the two approaches aremore complementary than substitutes, since MF-VAR tends to perform better for longerhorizons, whereas MIDAS for shorter horizons.
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