Sparse High-Dimensional Vector Autoregressive Bootstrap (opens in new tab)

arXiv:2302.01233v3 Announce Type: replace-cross Abstract: 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...