Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure

• Tác giả: Alain Hecq, Luca Margaritella, Stephan Smeekes
• Ngôn ngữ: eng
• Ký hiệu phân loại: 001.434 Experimental method
• Thông tin xuất bản: 2019
• Bộ sưu tập: Báo, Tạp chí
• ID: 162658

Tóm tắt

We develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in high-dimensional compared to standard low-dimensional VARs.