We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and parameter estimation by maximum likelihood and Bayesian methods. An illustration is provided for the analysis of a large panel of macroeconomic time series.
EM algorithm Kalman Filter Forecasting Latent Factors Markov chain Monte Carlo Principal Components State Space