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

Alain Hecq, Luca Margaritella and Stephan Smeekes

Journal of Financial Econometrics, 2023, vol. 21, issue 3, 915-958

Abstract: We develop an LM test for Granger causality in high-dimensional (HD) vector autoregressive (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 HD compared to standard low-dimensional VARs.

Keywords: Granger causality; high-dimensional inference; post-double-selection; vector autoregressive models (search for similar items in EconPapers)
JEL-codes: C12 C32 C55 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure (2020)
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