Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

Alain Hecq, Marie Ternes and Ines Wilms

Papers from arXiv.org

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.

Date: 2021-02, Revised 2022-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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