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

6 Scopus citations


There has been 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)
Pages (from-to)532-557
Number of pages26
JournalJournal of Time Series Analysis
Issue number4
StatePublished - Jul 2022

All Science Journal Classification (ASJC) codes

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


  • VAR
  • high-dimensional time series
  • mixing


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