Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/214842
Authors: 
Lucchetti, Riccardo
Venetis, Ioannis A.
Year of Publication: 
2020
Series/Report no.: 
Economics Discussion Papers No. 2020-5
Abstract: 
The authors replicate and extend the Monte Carlo experiment presented in Doz et al. (2012) on alternative (time-domain based) methods for extracting dynamic factors from large datasets; they employ open source software and consider a larger number of replications and a wider set of scenarios. Their narrow sense replication exercise fully confirms the results in the original article. As for their extended replication experiment, the authors examine the relative performance of competing estimators under a wider array of cases, including richer dynamics, and find that maximum likelihood (ML) is often the dominant method; moreover, the persistence characteristics of the observable series play a crucial role and correct specification of the underlying dynamics is of paramount importance.
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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