Cluster-Based Regularized Sliced Inverse Regression for Forecasting Macroeconomic Variables

TL;DR

This paper introduces a novel cluster-based regularized sliced inverse regression method for dimension reduction in macroeconomic forecasting, effectively handling correlated variables and outperforming existing models.

Contribution

The paper proposes a new regularized sliced inverse regression technique that improves dimension reduction and variable handling in macroeconomic data analysis.

Findings

Outperforms dynamic factor model in empirical tests

Handles highly correlated variables effectively

Enhances dimension reduction in macroeconomic forecasting

Abstract

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.