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

Author

Listed:
  • Alain Hecq
  • Luca Margaritella
  • Stephan Smeekes

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.

Suggested Citation

  • Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Oct 2019.
  • Handle: RePEc:arx:papers:1902.10991
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    References listed on IDEAS

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    Cited by:

    1. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org.
    2. Mansour-Ichrakieh, Layal, 2020. "The impact of Israeli Geopolitical Risks on the Lebanese Financial Market: A Destabilizer Multiplier," MPRA Paper 99376, University Library of Munich, Germany.
    3. Gianluca Cubadda & Alain Hecq, 2020. "Dimension Reduction for High Dimensional Vector Autoregressive Models," Papers 2009.03361, arXiv.org.

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