Discovering the Network Granger Causality in Large Vector Autoregressive Models

• 成果类型: Article; Early Access
• 署名作者: Uematsu, Yoshimasa; Yamagata, Takashi
• 署名单位: Hitotsubashi University; University of York - UK; University of Osaka
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2450836
• 发表日期: 2025
• 关键词: confidence-intervals; conditional heteroskedasticity; Wild Bootstrap; time-series; parameters

摘要

This article proposes novel inferential procedures for discovering the network Granger causality in high-dimensional vector autoregressive models. In particular, we mainly offer two multiple testing procedures designed to control the false discovery rate (FDR). The first procedure is based on the limiting normal distribution of the t-statistics with the debiased lasso estimator. The second procedure is its bootstrap version. We also provide a robustification of the first procedure against any cross-sectional dependence using asymptotic e-variables. Their theoretical properties, including FDR control and power guarantee, are investigated. The finite sample evidence suggests that both procedures can successfully control the FDR while maintaining high power. Finally, the proposed methods are applied to discovering the network Granger causality in a large number of macroeconomic variables and regional house prices in the United Kingdom. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.