A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)

Riccardo (Jack) Lucchetti and Ioannis Venetis ()

No 2020-5, Economics Discussion Papers from Kiel Institute for the World Economy (IfW Kiel)

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.

Keywords: dynamic factor models; EM algorithm; Kalman filter; principal components (search for similar items in EconPapers)
JEL-codes: C15 C32 C55 C87 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifwedp:20205

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