Markov-Switching Three-Pass Regression Filter
提出一种马尔可夫切换三阶段回归滤波器，用于估计高维因子模型在因子载荷存在体制切换时的参数，适用于大截面数据，并在预测经济活动和汇率中表现良好。
We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass regression filter (MS-3PRF), is suitable for datasets with large cross-sectional dimensions, since estimation and inference are straightforward, as opposed to existing regime-switching factor models where computational complexity limits applicability to few variables. In a Monte Carlo experiment, we study the finite sample properties of the MS-3PRF and find that it performs favorably compared with alternative modeling approaches whenever there is structural instability in factor loadings. For empirical applications, we consider forecasting economic activity and bilateral exchange rates, finding that the MS-3PRF approach is competitive in both cases. Supplementary materials for this article are available online.