  This article concerns the dimension reduction in regression for large data
set. We introduce a new method based on the sliced inverse regression approach,
called cluster-based regularized sliced inverse regression. Our method not only
keeps the merit of considering both response and predictors' information, but
also enhances the capability of handling highly correlated variables. It is
justified under certain linearity conditions. An empirical application on a
macroeconomic data set shows that our method has outperformed the dynamic
factor model and other shrinkage methods.
