Sparse High-Dimensional Vector Autoregressive Bootstrap

Robert Adamek, Stephan Smeekes and Ines Wilms

Papers from arXiv.org

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.

Date: 2023-02
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2302.01233

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