A Note on Adaptive Group Lasso for Structural Break Time Series

Simon Behrendt and Karsten Schweikert

Econometrics and Statistics, 2021, vol. 17, issue C, 156-172

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 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:17:y:2021:i:c:p:156-172

DOI: 10.1016/j.ecosta.2020.04.001

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