Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations

Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes

Research output: Contribution to journalArticlepeer-review

Abstract

There has been-50 considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations. In contrast to current literature, our innovation process satisfy an L1 mixingale type condition on the centered conditional covariance matrices. This condition covers L1-NED sequences and strong ((Formula presented.) -) mixing sequences as particular examples.

Original languageEnglish (US)
JournalJournal of Time Series Analysis
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • high-dimensional time series
  • LASSO
  • mixing
  • VAR

Fingerprint

Dive into the research topics of 'Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations'. Together they form a unique fingerprint.

Cite this