🤖 AI Summary
This study addresses the challenge of simultaneously identifying structural break points and sparse active predictors in high-dimensional regression settings. The authors propose a three-stage procedure: first, active variables are screened via Sure Independence Canonical Screening (SICS); second, potential break points are estimated using Ratio-Controlled Regression Screening (RCRS); and third, an information criterion is employed to eliminate redundant variables and spurious breaks. The method accommodates a growing number of break points with sample size and is compatible with both stationary and cointegrated sparse predictors, enabling consistent selection and estimation of true break locations and active variables. Simulation studies and empirical analyses demonstrate that the approach achieves high accuracy and robustness in detecting both relevant predictors and structural breaks.
📝 Abstract
Predictive regression is a crucial tool for exploring return predictability. In this study, we introduce an efficient procedure for selecting and estimating active predictors and change points in structural break predictive regression. Our approach allows the number of change points to increase with the sample size and accommodates sparse active predictors that may be stationary or cointegrated. We begin by identifying the active predictors using a Sure Independence Canonical Screening (SICS) procedure. Next, we estimate the change points through a Ratio-Controlled Regression Screening (RCRS) method. Finally, we reduce redundancy by eliminating unnecessary breakpoints and predictors using information criteria (IC). This approach allows for consistent estimation and selection of true breakpoints and active predictors. Our simulations and empirical studies demonstrate that the proposed procedure performs effectively.