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

Alain Hecq, Luca Margaritella and Stephan Smeekes

Papers from arXiv.org

Abstract: 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.

Date: 2019-02, Revised 2020-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (9)

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Journal Article: Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure* (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1902.10991

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