Back to Search
Start Over
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions.
- Source :
-
Journal of Computational & Graphical Statistics . Oct-Dec2022, Vol. 31 Issue 4, p1076-1090. 15p. - Publication Year :
- 2022
-
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. for this article are available online. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COVARIANCE matrices
UNITED States economy
Subjects
Details
- Language :
- English
- ISSN :
- 10618600
- Volume :
- 31
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Computational & Graphical Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 160402974
- Full Text :
- https://doi.org/10.1080/10618600.2022.2058003