  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.
