arXiv 10 Jun 2026 · Econometrics
arXiv:2606.12261 · PDF · DOI · OpenAlex · Extracted main text
The package rbreak implements methods for detecting structural breaks and estimating break locations for linear multiple regression models under general linear restrictions on the coefficient vector. Restrictions can be within regimes, across regimes, or both, and are supported in two forms: an affine parameterization (Form A: delta = S*theta + s) and explicit linear constraints (Form B: R*delta = r). It provides break date estimation with confidence interval, a restricted sup-F test for the null of no structural change, simulation of critical values by Monte Carlo, and a bootstrap restart procedure to reduce the risk of convergence to spurious local optima. It also implements a generalized regression tree (linear model tree) procedure where each leaf contains a linear regression rather than a local average. This note explains the methods and illustrates them with applications.
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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 \ Perron (2003) `Computation and analysis of multiple structural change models', Journal of Applied Econometrics 18(1), 1–22 | 1.000 | 6 | 3 | 100% |
| 2 | Perron \ Qu (2006) `Estimating restricted structural change models', Journal of Econometrics 134(2), 373–399 | 0.874 | 14 | 2 | 100% |
| 3 | Bai \ Perron (1998) `Estimating and testing linear models with multiple structural changes', Econometrica 66(1), 47–78 | 0.737 | 3 | 2 | 100% |
| 4 | Quinlan (1992) Learning with continuous classes, in `Proceedings of the 5th Australian Joint Conference on Artificial Intelligence', World Scie… | 0.737 | 3 | 2 | 100% |
| 5 | Wood (2001) `Minimizing model fitting objectives that contain spurious local minima by bootstrap restarting', Biometrics 57(1), 240–244 | 0.644 | 2 | 2 | 100% |
| 6 | Breiman, Friedman, Olshen \ Stone (1984) Classification and Regression Trees, Wadsworth, Belmont, CA | 0.585 | 3 | 1 | 100% |
| 7 | Nikolsko-Rzhevskyy, Papell \ Prodan (2014) `Deviations from rules-based policy and their effects', Journal of Economic Dynamics and Control 49, 4–17 | 0.511 | 2 | 1 | 100% |
| 8 | Raymaekers, Rousseeuw, Verdonck \ Yao (2024) `Fast linear model trees by pilot', Machine Learning 113, 6561–6610 | 0.511 | 2 | 1 | 100% |
| 9 | Wang \ Witten (1997) Induction of model trees for predicting continuous classes, in `Proceedings of the European Conference on Machine Learning', Pra… | 0.511 | 2 | 1 | 100% |
| 10 | Zeileis, Hothorn \ Hornik (2008) `Model-based recursive partitioning', Journal of Computational and Graphical Statistics 17(2), 492–514 | 0.511 | 2 | 1 | 100% |
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