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| Format: | Preprint |
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2022
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| Online Access: | https://arxiv.org/abs/2201.05430 |
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| _version_ | 1866914954922164224 |
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| author | Schweikert, Karsten |
| author_facet | Schweikert, Karsten |
| contents | In this paper, we propose a two-step procedure based on the group LASSO estimator in combination with a backward elimination algorithm to detect multiple structural breaks in linear regressions with multivariate responses. Applying the two-step estimator, we jointly detect the number and location of structural breaks, 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) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_05430 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors Schweikert, Karsten Econometrics In this paper, we propose a two-step procedure based on the group LASSO estimator in combination with a backward elimination algorithm to detect multiple structural breaks in linear regressions with multivariate responses. Applying the two-step estimator, we jointly detect the number and location of structural breaks, 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) 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. |
| title | Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors |
| topic | Econometrics |
| url | https://arxiv.org/abs/2201.05430 |