Comparisons of four methods for estimating a dynamic factor model

Four methods for estimating a dynamic factor model, (lie direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based oil its state space model representation. The second one employs the maximum likelihood estimation method based oil the construction of a block-Toeplitz covariance matrix in the structural equation modeling framework. The third method is built in the Bayesian framework and implemented using Gibbs sampling. The fourth is the]cast squares method, which also employs the block-Toeplitz matrix. All 4 methods are implemented in Currently available software. The Simulation Study shows that all 4 methods reach appropriate parameter estimates with comparable precision. Differences among the 4 estimation methods and related software are discussed.