SFB649DP2017 022
Penalized Adaptive Method in Forecasting with Large Information Set and
Structure Change
Xinjue Li
Lenka Zbonakova
Wolfgang Karl Härdle
Abstract:
In the present paper we propose a new method, the Penalized Adaptive
Method (PAM), for a data driven detection of structure 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
help of penalized regression models. The method is simple yet flexible 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 selects proper significant
variables and detects structure breaks while steadily reduces the forecast error
in high-dimensional data. When applying PAM to bond risk premia modelling,
the locally selected variables and their estimated coefficient loadings identified
in the longest stable subsamples over time align with the true structure changes
observed throughout the market.
Keywords:
SCAD penalty, propagation-separation, adaptive window choice, multiplier bootstrap,
bond risk premia
JEL Classification:
C13, C20, E37