The estimation of dynamic factor models for large cross-sections poses a challenge in a real time environment. As macroeconomic data become available with different delays, unbalanced panel data sets with missing values at the end of the sample period (the so-called "jagged edge") have to be handled when estimating the factor model. In this paper, we propose an EM algorithm which copes with such data sets, accounts for autoregressive common factors and allows 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.