Estimation and inference for high dimensional factor model with regime switching
提出高维因子模型在载荷体制转换下的最大似然估计方法，通过EM算法迭代计算体制概率和主成分，并证明估计量的一致性、收敛速度和极限分布，可用于检测商业周期转折点。
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 identiÖed 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.