Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

A. Hecq, M. Ternes*, I. Wilms

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Number of pages15
JournalJournal of Computational and Graphical Statistics
DOIs
Publication statusE-pub ahead of print - 7 May 2022

Keywords

  • Coincident indicators
  • Group lasso
  • High-dimensionality
  • Variable selection
  • GRANGER CAUSALITY
  • INFERENCE
  • SELECTION
  • MIDAS
  • GDP

Cite this