Artem Prokhorov, Peter Radchenko, Alexander Semenov, Anton Skrobotov
arXiv 11 Aug 2024 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 2 citations (OpenAlex)
arXiv:2408.05665 · PDF · DOI · OpenAlex · Extracted main text
We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the $l_0$-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding provably optimal solutions using a well-known optimization solver. Compared to the popular $l_1$-penalized regression (LASSO) and other classical methods, the MIO framework permits simultaneous estimation of the number and location of structural breaks as well as regression coefficients, while accommodating the option of specifying a given or minimal number of breaks. We derive the asymptotic properties of the estimator and demonstrate its effectiveness through extensive numerical experiments, confirming a more accurate estimation of multiple breaks as compared to popular non-MIO alternatives. Two empirical examples demonstrate usefulness of the framework in applications from business and economic statistics.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bai, J. and Perron, P (1998) Estimating and testing linear models with multiple structural changes | 1.000 | 7 | 3 | 100% |
| 2 | Bai, J. and Perron, P (2003) Computation and analysis of multiple structural change models | 1.000 | 5 | 3 | 100% |
| 3 | Qian, J. and Su, L (2016) Shrinkage estimation of regression models with multiple structural changes | 0.896 | 32 | 5 | 72% |
| 4 | Bertsimas, D., King, A., and Mazumder, R (2016) Best subset selection via a modern optimization lens | 0.843 | 3 | 3 | 100% |
| 5 | Hazimeh, H., Mazumder, R., and Radchenko, P (2023) Grouped variable selection with discrete optimization: Computational and statistical perspectives self | 0.843 | 3 | 3 | 100% |
| 6 | Jorda, O. and Marcellino, M (2004) Time-scale transformations of discrete time processes | 0.693 | 8 | 1 | 100% |
| 7 | Jorda, O (1999) Random-time aggregation in partial adjustment models | 0.693 | 5 | 1 | 100% |
| 8 | Kaddoura, Y. and Westerlund, J (2023) Estimation of panel data models with random interactive effects and multiple structural breaks when t is fixed | 0.644 | 2 | 2 | 100% |
| 9 | Mazumder, R., Radchenko, P., and Dedieu, A (2023) Subset selection with shrinkage: Sparse linear modeling when the SNR is low self | 0.644 | 2 | 2 | 100% |
| 10 | Chan, N. H., Yau, C. Y., and Zhang, R.-M (2014) Group lasso for structural break time series | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 28 scored citations.