Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data
摘要
The availability of many variables with predictive power makes their selection in a regression context difficult. This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks. Our new algorithm is based on generalized cross-validation and builds a predictive model step-by-step from a simple mean to more complex predictive combinations. Empirical applications to annual financial returns and actuarial telematics data show its usefulness in the financial and insurance industries.