Discovering the Network Granger Causality in Large Vector Autoregressive Models
本文提出了两种控制错误发现率的多重检验方法，用于在高维向量自回归模型中识别网络格兰杰因果关系，并通过宏观经济数据和英国房价数据验证了其有效性。
This article proposes novel inferential procedures for discovering the network Granger causality in highdimensional 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.