City Research Online

Abstract

Variable correlations pose significant challenges for various LASSO-type shrinkage methods in big-data modeling. This paper presents a comprehensive framework for a correlation-robust shrinkage estimator, enhancing both the theoretical and practical aspects of high-dimensional estimation. We break down the penalty term into a “shrinkage” term and a “grouping” term, illustrating its distinctive properties in clustering highly correlated variables. We establish the (non-)asymptotic properties of this estimator under relaxed assumptions regarding mixing conditions and heavier-tailed distributions. Furthermore, we demonstrate the consistency of model selection under mild conditions. Additionally, we propose a debiased version of the estimator, proving its asymptotic normality. Simulated data indicate that the debiased estimator surpasses traditional benchmarks. In an empirical application, we use this debiased estimator to identify key Economic Policy Uncertainty (EPU) factors that account for inflation levels. Our findings imply that news-based EPU factors are essential in explaining CPI dynamics.

Publication Type:	Article
Additional Information:	© 2026 Published by Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	LASSO, Correlated variables, Debiased estimator, Shrinkage
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School Bayes Business School > Faculty of Finance
SWORD Depositor:	Symplectic Administrator

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