A comment on the dynamic factor model with dynamic factors - Pilar Poncela; Esther Ruiz (Kiel Institute for the World Economy (IfW), 2020)
In this paper, the authors comment on the Monte Carlo results of the paper by Lucchetti and Veneti (A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics), 2020)) that studies and compares the performance of the Kalman Filter and Smoothing (KFS) and Principal Components (PC) factor extraction procedures in the context of Dynamic Factor Models (DFMs). The new Monte Carlo results of Lucchetti and Veneti (2020) refer to a DFM in which the relation between the factors and the variables in the system is not only contemporaneous but also lagged. The authors main point is that, in this context, the model specification, which is assumed to be known in Lucchetti and Veneti (2020), is important for the properties of the estimated factors. Furthermore, estimation of the parameters is also problematic in some cases.
Keywords: C15; C32; C55; C87; 330; Dynamic factor models; EM algorithm; Kalman filter; principal components