Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

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 thro...

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Bibliographic Details
Published in:Journal of computational and graphical statistics 2022-10, Vol.31 (4), p.1076-1090
Main Authors: Hecq, Alain, Ternes, Marie, Wilms, Ines
Format: Article
Language:English
Subjects:
Coincident indicators
Covariance matrix
Error analysis
Group lasso
High-dimensionality
Variable selection
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Summary: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.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2022.2058003