Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/216838
Authors: 
Poncela, Pilar
Ruiz, Esther
Year of Publication: 
2020
Series/Report no.: 
Economics Discussion Papers No. 2020-7
Abstract: 
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.
Subjects: 
Dynamic factor models
EM algorithm
Kalman filter
principal components
JEL: 
C15
C32
C55
C87
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Working Paper

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