Detecting Multiple Structural Breaks in Systems of Linear Regression Equations With Integrated and Stationary Regressors
提出一种基于组LASSO估计和向后消除算法的两步法，用于检测多元线性回归中的多个结构断点，该方法允许混合整合和平稳回归变量，计算效率高，并通过利率期限结构的经济应用加以说明。
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.