Penalized Adaptive Forecasting With Large Information Sets and Structural Changes

Abstract

In the present paper we propose a new method, the Penalized Adaptive Method (PAM), for a data driven detection of structural changes in sparse linear models. The method is able to allocate the longest homogeneous intervals over the data sample and simultaneously choose the most proper variables with the help of penalized regression models. The method is simple yet exible and can be safely applied in high-dimensional cases with different sources of parameter changes. Comparing with the adaptive method in linear models, its combination with dimension reduction yields a method which properly selects significant variables and detects structural breaks while steadily reduces the forecast error in high-dimensional data.