A Note on Adaptive Group Lasso for Structural Break Time Series

Behrendt, Simon; Schweikert, Karsten

doi:10.1016/j.ecosta.2020.04.001

Cited by 8 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, we show that group lasso is a consistent estimator for non-zero parameter changes giving us appropriate weights for a second step adaptive group lasso estimation. This approach is similar to the ideas put forth in Wei and Huang (2010), Horowitz and Huang (2013), Schmidt and Schweikert (2021) and Behrendt and Schweikert (2021).…”

Section: Frameworkmentioning

confidence: 82%

“…Addressing this issue, Bai and Perron (1998,2003) use dynamic programming techniques (henceforth Bai–Perron algorithm), requiring at most least‐squares operations of order O ( T 2 ) for any number of breaks, to add breaks sequentially to the model. Recently, several approaches have been proposed that reframe the task of detecting and estimating structural breaks as a model selection problem employing penalized regressions and related model selection techniques (Davis et al 2006; Harchaoui and Lévy‐Leduc, 2010; Jin et al 2013; Chan et al 2014; Ciuperca, 2014; Jin et al 2016; Qian and Jia, 2016; Qian and Su, 2016; Behrendt and Schweikert, 2021). Instead of grid search procedures which augment linear regression models with parameter changes, model selection procedures take a top‐down approach and try to shrink the set of all possible breakpoint candidates to contain only the true breakpoints.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions

Schweikert

2021

Journal Time Series Analysis

Self Cite

View full text Add to dashboard Cite

In this article, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long-run US money demand function demonstrates the practical importance of this methodology.

show abstract

Section: Frameworkmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions

Schweikert

2021

Journal Time Series Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…To obtain a consistent estimator for the number of breaks, their timing and coefficient changes, we need to design a second step refinement reducing the number of superfluous breaks. Immediate candidates are using a backward elimination algorithm (BEA) optimizing some information criterion (Chan et al, 2014;Gao et al, 2019;Safikhani and Shojaie, 2020) or applying the adaptive group LASSO estimator as a second step using the group LASSO estimates as weights (Behrendt and Schweikert, 2021;Schweikert, 2021). In the following, we outline the former approach and discuss the latter approach in the Supplementary Material because the BEA produces more accurate results in this setting.…”

Section: Second Step Estimatormentioning

confidence: 99%

“…Ciuperca (2014), Jin et al (2016) and Qian and Su (2016b) consider LASSO-type estimators for the detection of multiple structural breaks in linear regressions. Behrendt and Schweikert (2021) propose an alternative strategy to eliminate superfluous breakpoints identified by the group LASSO estimator. They suggest a second step adaptive group LASSO which performs comparably to the backward elimination algorithm suggested in Chan et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Efficiently Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors

Schweikert¹

2022

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a two-step procedure based on the group LASSO estimator in combination with a backward elimination algorithm to efficiently detect multiple structural breaks in linear regressions with multivariate responses. Applying the twostep estimator, we jointly detect the number and location of change points, and provide consistent estimates of the coefficients. Our framework is flexible enough to allow for a mix of integrated and stationary regressors, as well as deterministic terms. Using simulation experiments, we show that the proposed two-step estimator performs competitively against the likelihood-based approach (Qu and Perron, 2007;Li and Perron, 2017;Oka and Perron, 2018) when trying to detect common breaks in finite samples. However, the two-step estimator is computationally much more efficient. An economic application to the identification of structural breaks in the term structure of interest rates illustrates this methodology.

show abstract

“…Several methods are used to model structural breaks, each offering unique advantages. These include Markov Switching, the threshold model, adaptive group lasso, time-varying parameters, and a combination of Realised Volatility and Heterogeneous Autoregressive methods, as implemented by [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export

Sasmita,

Kuswanto,

Prastyo

2024

Forecasting

View full text Add to dashboard Cite

Standard time-series modeling requires the stability of model parameters over time. The instability of model parameters is often caused by structural breaks, leading to the formation of nonlinear models. A state-dependent model (SDM) is a more general and flexible scheme in nonlinear modeling. On the other hand, time-series data often exhibit multiple frequency components, such as trends, seasonality, cycles, and noise. These frequency components can be optimized in forecasting using Singular Spectrum Analysis (SSA). Furthermore, the two most widely used approaches in SSA are Linear Recurrent Formula (SSAR) and Vector (SSAV). SSAV has better accuracy and robustness than SSAR, especially in handling structural breaks. Therefore, this research proposes modeling the SSAV coefficient with an SDM approach to take structural breaks called SDM-SSAV. SDM recursively updates the SSAV coefficient to adapt over time and between states using an Extended Kalman Filter (EKF). Empirical results with Indonesian Export data and simulation studies show that the accuracy of SDM-SSAV outperforms SSAR, SSAV, SDM-SSAR, hybrid ARIMA-LSTM, and VARI.

show abstract

A Note on Adaptive Group Lasso for Structural Break Time Series

Cited by 8 publications

References 36 publications

Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions

Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions

Efficiently Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors

State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export

Contact Info

Product

Resources

About