Rbreak: An R Package for Estimating Structural Breaks Under Linear Restrictions with Application to Linear Model Tree

Abstract 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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Publication Details

Journal
Journal of Econometric Methods
Published
2026-09-09
DOI
https://doi.org/10.1515/jem-2026-0017
Primary Topic
Statistical Methods and Inference
Type
article
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article

Rbreak: An R Package for Estimating Structural Breaks Under Linear Restrictions with Application to Linear Model Tree

Zhongjun Qu, Cheolju Kim
Journal of Econometric Methods
Statistical Methods and Inference
article

Rbreak: An R Package for Estimating Structural Breaks Under Linear Restrictions with Application to Linear Model Tree

Zhongjun Qu, Cheolju Kim
article en

Abstract

Abstract 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.

Journal of Econometric Methods
Boston University (US), University of Massachusetts Boston (US)
Reduced inequalities
Openalex Percentile: Top 42%
Statistical Methods and Inference
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