Working Paper: CEPR ID: DP7445
Authors: Vladimir Kuzin; Massimiliano Marcellino; Christian Schumacher
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 based on exponential lag polynomials for the coefficients, whereas MF-VAR does not restrict the dynamics and therefore can suffer from the curse of dimensionality. But 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 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.
Keywords: Euro Area; Growth; MIDAS; Mixed-Frequency Data; Mixed-Frequency VAR; Nowcasting
JEL Codes: C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| MIDAS (Y20) | GDP growth accuracy (O47) |
| MFVAR (C39) | GDP growth accuracy (O47) |
| Autoregressive dynamics in MIDAS (C22) | GDP growth accuracy (O47) |