论文标题
使用政权切换的高维因子模型的估计和推断
Estimation and Inference for High Dimensional Factor Model with Regime Switching
论文作者
论文摘要
本文提出了高维因子模型模型的最大（准）可能性估计，并在负载中进行了更换。模型参数由EM（期望最大化）算法共同估算，在当前情况下，该算法仅需要迭代计算加权样品协方差矩阵的制度概率和主要成分。考虑到制度动力学时，使用递归算法计算平滑的制度概率。估计负载和估计因子的一致性，收敛速率和限制分布在弱的横截面和时间依赖性以及异质性下建立。值得注意的是，由于高度，在切换点之后只能以一个观测值来始终如一地识别机制切换。仿真结果表明了该方法的良好性能。 FRED-MD数据集的应用程序说明了提出的方法检测商业周期转折点的潜力。
This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with regime switching in the loadings. The model parameters are estimated jointly by the EM (expectation maximization) algorithm, which in the current context only requires iteratively calculating regime probabilities and principal components of the weighted sample covariance matrix. When regime dynamics are taken into account, smoothed regime probabilities are calculated using a recursive algorithm. Consistency, convergence rates and limit distributions of the estimated loadings and the estimated factors are established under weak cross-sectional and temporal dependence as well as heteroscedasticity. It is worth noting that due to high dimension, regime switching can be identified consistently after the switching point with only one observation. Simulation results show good performance of the proposed method. An application to the FRED-MD dataset illustrates the potential of the proposed method for detection of business cycle turning points.
