Modified LASSO estimators for time series regression models with dependent disturbances

Yujie Xue*, Masanobu Taniguchi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper applies the modified least absolute shrinkage and selection operator (LASSO) to the regression model with dependent disturbances, especially, long-memory disturbances. Assuming the norm of different column in the regression matrix may have different order of observation length n, we introduce a modified LASSO estimator where the tuning parameter λ is not a scalar but vector. When the dimension of parameters is fixed, we derive the asymptotic distribution of the modified LASSO estimators under certain regularity condition. When the dimension of parameters increases with respect to n, the consistency on the probability of the correct selection of penalty parameters is shown under certain regularity conditions. Some simulation studies are examined.

Original languageEnglish
Pages (from-to)845-869
Number of pages25
JournalStatistical Methods and Applications
Issue number4
Publication statusPublished - 2020 Dec


  • High dimensional regression
  • Long-memory disturbances
  • Modified LASSO

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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