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

Alain Hecq, Luca Margaritella, Stephan Smeekes

arXiv 28 Feb 2019 · Econometrics · publishedJournal of Financial Econometrics (2021) · 5 citations (OpenAlex)

arXiv:1902.10991 · PDF · DOI · OpenAlex · Extracted main text

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.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Kock, A. B. and L. Callot (2015) Oracle inequalities for high dimensional vector autoregressions1.00064100%
2Masini, R. P., M. C. Medeiros, and E. F. Mendes (2019) Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations1.00053100%
3Medeiros, M. C. and E. F. Mendes (2016) $_1$-regularization of high-dimensional time-series models with non-gaussian and heteroskedastic errors1.00053100%
4Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls0.9285380%
5Belloni, A., V. Chernozhukov, and C. Hansen (2014) High-dimensional methods and inference on structural and treatment effects0.84333100%
6Granger, C. W (1969) Investigating causal relations by econometric models and cross-spectral methods0.81142100%
7Belloni, A. and V. Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models0.73732100%
8Wong, K. C., Z. Li, and A. Tewari (2020) Lasso guarantees for $$-mixing heavy-tailed time series0.73732100%
9Zou, H. and T. Hastie (2005) Regularization and variable selection via the elastic net0.73732100%
10Basu, S., A. Shojaie, and G. Michailidis (2015) Network granger causality with inherent grouping structure0.64422100%

Showing the top 10 of 77 scored citations.

Cited by, within the corpus

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12410.043300.92843
2Inference in Non-stationary High-Dimensional VARs0.73732
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4Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?0.64422
5High-Dimensional Granger Causality for Climatic Attribution0.58531
6Min(d)ing the President: A text analytic approach to measuring tax news0.51122
7Lasso Inference for High-Dimensional Time Series0.40511
8Machine Learning Advances for Time Series Forecasting0.40511
9Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions0.40511
10Econometrics of Machine Learning Methods in Economic Forecasting0.40511