Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/214842 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
Economics Discussion Papers No. 2020-5
Verlag: 
Kiel Institute for the World Economy (IfW), Kiel
Zusammenfassung: 
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.
Schlagwörter: 
dynamic factor models
EM algorithm
Kalman filter
principal components
JEL: 
C15
C32
C55
C87
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
314.8 kB





Publikationen in EconStor sind urheberrechtlich geschützt.