# Sparse High-Dimensional Vector Autoregressive Bootstrap

arXiv: https://arxiv.org/abs/2302.01233 · HTML: https://mlsanity.com/paper/2302.01233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [Concentration inequalities for high-dimensional linear processes with dependent innovations](https://mlsanity.com/paper/2307.12395/) (2023)

## Abstract (arXiv)

We introduce a high-dimensional multiplier bootstrap for time series data based on 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.