  Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between
variables recorded at different frequencies. However, as the number of series
and high-frequency observations per low-frequency period grow, MF-VARs suffer
from the "curse of dimensionality". We curb this curse through a regularizer
that permits various hierarchical sparsity patterns by prioritizing the
inclusion of coefficients according to the recency of the information they
contain. Additionally, we investigate the presence of nowcasting relations by
sparsely estimating the MF-VAR error covariance matrix. We study predictive
Granger causality relations in a MF-VAR for the U.S. economy and construct a
coincident indicator of GDP growth.
