Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/219103
Authors: 
Lucchetti, Riccardo
Venetis, Ioannis A.
Year of Publication: 
2020
Citation: 
[Journal:] Economics: The Open-Access, Open-Assessment E-Journal [ISSN:] 1864-6042 [Year:] 2020 [Volume:] 14 [Issue:] 2020-14 [Pages:] 1-14
Abstract: 
The authors replicate and extend the Monte Carlo experiment presented in Doz, Giannone and Reichlin (A Quasi-Maximum Likelihood Approach For Large, Approximate Dynamic Factor Models, Review of Economics and Statistics, 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
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

Files in This Item:
File
Size
237.59 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.