GLS Estimation of Dynamic Factor Models
In this article a simple two-step estimation procedure of the dynamic factor model is proposed. The estimator allows for heteroscedastic and serially correlated errors. It turns out that the feasible two-step estimator has the same limiting distribution as the generalized least squares (GLS) estimator assuming that the covariance parameters are known. In a Monte Carlo study of the small sample properties, we find that the GLS estimators may be substantially more efficient than the usual estimator based on principal components. Furthermore, it turns out that the iterated version of the estimator may feature considerably improved properties in sample sizes usually encountered in practice.