Sparse High-Dimensional Vector Autoregressive Bootstrap

Robert Adámek*, Stephan Smeekes, Ines Wilms

*Corresponding author for this work

Research output: Working paper / PreprintPreprint

Abstract

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

SeriesarXiv.org
Number2302.01233
ISSN2331-8422

Keywords

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

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