Estimation and inference of change points in high-dimensional factor models
Estimation and inference of change points in high-dimensional factor models
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DOI：
10.1016/j.jeconom.2019.08.013
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发表时间：
2020-11
影响因子：
6.3
通讯作者：
Jushan Bai;Xu Han;Yutang Shi
中科院分区：
文献类型：
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作者：
Jushan Bai;Xu Han;Yutang Shi
在这篇文章中，我们考虑了高维因子模型中的突变点估计问题，其中未观测因子是通过主成分分析(PCA)来估计的。假设因子加载矩阵在未知时间具有结构突变。我们建立了最小二乘(LS)估计对中断日期相容的条件。无论是大突破还是小突破，我们的一致性结果都成立。给出了最小二乘估计的渐近分布。仿真结果表明，即使在断裂量较小的情况下，最小二乘法也能准确估计出断裂点的大小。在两个实证应用中，我们分别实现了估计美国股市和美国宏观经济中断点的方法。
In this paper, we consider the estimation of break points in high-dimensional factor models where the unobserved factors are estimated by principal component analysis (PCA). The factor loading matrix is assumed to have a structural break at an unknown time. We establish the conditions under which the least squares (LS) estimator is consistent for the break date. Our consistency result holds for both large and small breaks. We also find the LS estimator’s asymptotic distribution. Simulation results confirm that the break date can be accurately estimated by the LS even if the magnitudes of breaks are small. In two empirical applications, we implement the method to estimate break points in the U.S. stock market and U.S. macroeconomy, respectively.