Sparse High-Dimensional Vector Autoregressive Bootstrap

• Tác giả: Robert Adamek, Stephan Smeekes, Ines Wilms
• Ngôn ngữ: eng
• Ký hiệu phân loại: 003.76 Stochastic systems
• Thông tin xuất bản: 2023
• Bộ sưu tập: Báo, Tạp chí
• ID: 196428

Tóm tắt

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.