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In this paper, we propose a two-step procedure based on the group LASSOestimator in combination with a backward elimination algorithm to detectmultiple structural breaks in linear regressions with multivariate responses.Applying the two-step estimator, we jointly detect the number and location ofchange points, and provide consistent estimates of the coefficients. Ourframework is flexible enough to allow for a mix of integrated and stationaryregressors, as well as deterministic terms. Using simulation experiments, weshow that the proposed two-step estimator performs competitively against thelikelihood-based approach (Qu and Perron, 2007; Li and Perron, 2017; Oka andPerron, 2018) in finite samples. However, the two-step estimator iscomputationally much more efficient. An economic application to theidentification of structural breaks in the term structure of interest ratesillustrates this methodology.

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