Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

TL;DR

This paper introduces a hierarchical regularizer for mixed-frequency VAR models to address high-dimensional challenges, improving nowcasting and causal inference in economic data.

Contribution

It proposes a novel hierarchical regularizer that enforces sparsity based on information recency, enhancing the estimation of high-dimensional MF-VARs.

Findings

Improved GDP growth nowcasting accuracy.

Identified key Granger causality relations in U.S. economic data.

Demonstrated effectiveness of hierarchical regularization in high-dimensional settings.

Abstract

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.