The LASSO-type shrinkage methods have become increasingly popular in the big
data era. However, variable correlations can significantly compromise the stability
and validity of such estimators. This paper advances the development of a correlation-
robust LASSO-type estimator.We develop the (non)asymptotic properties of the this
estimator under less restrictive conditions, including the α−mixing condition and
accommodating heavier tails than the standard i.i.d. sub-Gaussian setting. Further-
more, we propose a de-biased version of this estimator and establish its asymptotic
normality. Through simulated data, we demonstrate that the de-biased estimator
significantly reduces estimation errors. Empirically, we apply it to identify crucial
factors from the factor zoo, revealing that, despite high correlation with numerous
other factors, the ‘market' factor is the most influential in driving cross-sectional asset
returns. Our findings also highlight the significant impact of ‘liquidity,' ‘profitability,'
and ‘momentum'-related factors on cross-sectional asset returns.