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A Note on Adaptive Group Lasso for Structural Break Time Series

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  • Behrendt, Simon
  • Schweikert, Karsten

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.

Suggested Citation

  • Behrendt, Simon & Schweikert, Karsten, 2021. "A Note on Adaptive Group Lasso for Structural Break Time Series," Econometrics and Statistics, Elsevier, vol. 17(C), pages 156-172.
    • Handle: RePEc:eee:ecosta:v:17:y:2021:i:c:p:156-172
    DOI: 10.1016/j.ecosta.2020.04.001
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    3. Karsten Schweikert, 2022. "Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors," Papers 2201.05430, arXiv.org, revised Sep 2024.
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    5. Karsten Schweikert, 2022. "Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 83-104, January.

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