A Note on Adaptive Group Lasso for Structural Break Time Series

32 Pages Posted: 25 Nov 2019 Last revised: 31 Mar 2020

See all articles by Simon Behrendt

Simon Behrendt

Zeppelin University

Karsten Schweikert

University of Hohenheim

Date Written: March 15, 2020

Abstract

Considering structural break autoregressive (SBAR) processes and following recent literature, the problem of estimating the unknown number of change-points is cast as a model selection problem. The adaptive group Lasso is used to select the number of change-points for which parameter estimation consistency, model selection consistency, and asymptotic normality are proven. It is shown in simulation experiments that adaptive group Lasso performs comparably to a state-of-the-art two-step group Lasso procedure with backward elimination and other leading-edge approaches. Moreover, comparing the forecasting performance of both group Lasso procedures in an empirical application to realized variance dynamics, adaptive group Lasso is found to date change-points with equal accuracy. Thus, in practice, adaptive group Lasso can provide an alternative way to consistently select change-points in related applications.

Keywords: change-points, model selection, nonstationary autoregressive process, structural breaks

JEL Classification: C22, C52

Suggested Citation

Behrendt, Simon and Schweikert, Karsten, A Note on Adaptive Group Lasso for Structural Break Time Series (March 15, 2020). Available at SSRN: https://ssrn.com/abstract=3486104 or http://dx.doi.org/10.2139/ssrn.3486104

Simon Behrendt

Zeppelin University ( email )

Am Seemooser Horn 20
Friedrichshafen, Lake Constance 88045
Germany

Karsten Schweikert (Contact Author)

University of Hohenheim ( email )

Schloss 1C
Stuttgart, 70593
Germany

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
28
Abstract Views
169
PlumX Metrics