论文标题
在具有集成和固定回归器的线性回归方程系统中检测多个结构断裂
Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors
论文作者
论文摘要
在本文中，我们提出了一个基于组拉索估计量的两步程序，并结合向后消除算法，以检测具有多变量响应的线性回归中的多个结构断裂。应用两步估计器，我们共同检测结构断裂的数量和位置，并提供一致的系数估计值。我们的框架足够灵活，可以允许集成和固定回归器以及确定性术语结合使用。使用仿真实验，我们表明，提出的两步估计器在有限样本中针对基于可能性的方法进行了竞争性的竞争性（Qu and Perron，2007; Li and Perron，2017; Oka and Perron，2018）。但是，两步估计器在计算上的效率要高得多。在利率术语结构中识别结构中断的经济应用说明了这种方法。
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.
