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

Hecq, Alain ; Margaritella, Luca LU and Smeekes, Stephan (2023) In Journal of Financial Econometrics 21(3). p.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.
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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Granger causality, high-dimensional inference, post-double-selection, vector autoregressive models, C55, C12, C32
in
Journal of Financial Econometrics
volume
21
issue
3
pages
44 pages
publisher
Oxford University Press
external identifiers
  • scopus:85163332791
ISSN
1479-8417
DOI
10.1093/jjfinec/nbab023
language
English
LU publication?
yes
id
0da33691-5009-4804-a068-ece29123e6e6
date added to LUP
2021-11-04 13:42:29
date last changed
2023-10-26 14:59:40
@article{0da33691-5009-4804-a068-ece29123e6e6,
  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.}},
  author       = {{Hecq, Alain and Margaritella, Luca and Smeekes, Stephan}},
  issn         = {{1479-8417}},
  keywords     = {{Granger causality; high-dimensional inference; post-double-selection; vector autoregressive models; C55; C12; C32}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{915--958}},
  publisher    = {{Oxford University Press}},
  series       = {{Journal of Financial Econometrics}},
  title        = {{Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure}},
  url          = {{http://dx.doi.org/10.1093/jjfinec/nbab023}},
  doi          = {{10.1093/jjfinec/nbab023}},
  volume       = {{21}},
  year         = {{2023}},
}