Year: 2023
Type: preprint
Abstract: This paper 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 UK.
Source: arXiv (Cornell University)
Authors: Uematsu, Yoshimasa, Yamagata, Takashi
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Economic stagnation and widening wealth inequality: Crises of the world economy and a construction of a unified macroeconomic theory, Econometric analysis of time series and panel data using factor models and penalization methods: theory and applications, Construction of the global inference theory in high-dimensional macroeconometrics, Analysis of the carbon pricing policies and their impact on the global economy using counterfactual experiments, Behavioral Macroeconomics under Imperfect Information
OpenAlex ID: W4361194094