Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230750
Authors: 
Zbonakova, Lenka
Li, Xinjue
Härdle, Wolfgang Karl
Year of Publication: 
2018
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2018-039
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 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.
Subjects: 
SCAD penalty
propagation-separation
adaptive window choice
multiplier bootstrap
JEL: 
C12
C13
C50
E47
G12
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.