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• W3128113457 abstract "We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time series data structures we focus on the sparse-group LASSO estimator, which includes the LASSO and the group LASSO as special cases. We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals. This leads to valid time series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk-Nagaev inequality for a class of $tau$-mixing processes with heavier than Gaussian tails, which is of independent interest. In an empirical application, we study the Granger causal relationship between the VIX and financial news." @default.
• W3128113457 created "2021-02-15" @default.
• W3128113457 creator A5008717461 @default.
• W3128113457 creator A5051448393 @default.
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• W3128113457 date "2019-12-13" @default.
• W3128113457 modified "2023-09-27" @default.
• W3128113457 title "High-Dimensional Granger Causality Tests with an Application to VIX and News" @default.
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• W3128113457 hasPublicationYear "2019" @default.
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