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

TL;DR

This paper introduces a two-step group LASSO-based method for detecting multiple structural breaks in multivariate linear regression models with mixed regressors, offering improved computational efficiency and consistent estimation.

Contribution

It develops a novel two-step estimator combining group LASSO and backward elimination for joint detection of break points and coefficients in complex regression models.

Findings

Performs competitively with likelihood-based methods in simulations

Offers significant computational efficiency improvements

Successfully applied to interest rate term structure analysis

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 (Qu and Perron, 2007; Li and Perron, 2017; Oka and Perron, 2018) 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.