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Inference for high-dimensional regressions with heteroskedasticity and autocorrelation

By Andrii Babii and others
Time series regression analysis relies on the heteroskedasticity- and autocorrelation-consistent (HAC) estimation of the asymptotic variance to conduct proper inference. This paper develops such inferential methods for high-dimensional time series regressions. To recognize the time series data structures we focus on the sparse-group LASSO estimator. We establish the debiased central... Show more
May 25, 2020
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Inference for high-dimensional regressions with heteroskedasticity and autocorrelation
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