Sparse High-Dimensional Vector Autoregressive Bootstrap

Robert Adámek*, Stephan Smeekes, Ines Wilms

*Corresponding author for this work

Research output: Working paper / PreprintPreprint


We introduce a high-dimensional multiplier bootstrap for time series data based capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest.
Original languageEnglish
PublisherCornell University - arXiv
Number of pages44
Publication statusPublished - 2 Feb 2023

Publication series


  • High-dimensional data
  • Time series
  • Bootstrap
  • vector autoregression
  • linear process


Dive into the research topics of 'Sparse High-Dimensional Vector Autoregressive Bootstrap'. Together they form a unique fingerprint.

Cite this