Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Hecq, Alain; Ternes, Marie; Wilms, Ines
• Is Part Of: Journal of computational and graphical statistics. Volume 31:Issue 4(2022)
• Page Start: 1076
• Page End: 1090
• Publication Date: 2022-10-02
• Subjects: Coincident indicators -- Group lasso -- High-dimensionality -- Variable selection; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2022.2058003
• Languages: English
• ISSNs: 1061-8600

Abstract

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.