  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 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. Supplementary Materials for this article are available
online.
