Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models
研究了在动态因子模型中，当错误假设异质成分横截面不相关时，使用主成分分析、广义主成分分析及卡尔曼滤波等常用方法对因子点估计和区间估计的影响，发现忽略横截面依赖会增加估计不确定性并导致预测区间覆盖率极低。
In economics, Principal Components, its generalized version that takes into account heteroscedasticity, and Kalman filter and smoothing procedures are among the most popular procedures for factor extraction in the context of Dynamic Factor Models. This paper analyses the consequences on point and interval factor estimation of using these procedures when the idiosyncratic components are wrongly assumed to be cross-sectionally uncorrelated. We show that not taking into account the presence of cross-sectional dependence increases the uncertainty of point estimates of the factors. Furthermore, the Mean Square Errors computed using the usual expressions based on asymptotic approximations, are underestimated and may lead to prediction intervals with extremely low coverages.