Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 039

Penalized Adaptive Forecasting with Large Information Sets and Structural Changes

Lenka Zbonakova
Xinjue Li
Wolfgang Karl Härdle

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 dierent sources of parameter
changes. Comparing with the adaptive method in linear models, its combination
with dimension reduction yields a method which properly selects signicant
variables and detects structural breaks while steadily reduces the forecast error
in high-dimensional data.

SCAD penalty, propagation-separation, adaptive window choice, multiplier bootstrap

JEL Classification:
C12, C13, C50, E47, G12