Detecting multiple structural breaks in systems of linear regression equations with integrated and stationary regressors

Abstract

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 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.