Dynamic Factor Models with Jagged Edge Panel Data: Taking on Board the Dynamics of the Idiosyncratic Components
针对宏观经济数据发布延迟导致的面板数据末端缺失问题，提出一种EM算法，在估计动态因子模型时同时处理自回归公共因子和异质成分的序列相关，蒙特卡洛模拟表明考虑异质成分动态能显著提升末端缺失值和公共因子的估计精度。
Abstract As macroeconomic data are released with different delays, one has to handle unbalanced panel data sets with missing values at the end of the sample period when estimating dynamic factor models. We propose an EM algorithm which copes with such data sets while accounting for autoregressive common factors and allowing for serial correlation in the idiosyncratic components. Based on Monte Carlo simulations, we find that taking on board the dynamics of the idiosyncratic components improves significantly the accuracy of the estimation of both the missing values and the common factors at the end of the sample period.