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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 ()

Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), 2020, vol. 14, No 2020-14, 14 pages

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.

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
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http://dx.doi.org/10.5018/economics-ejournal.ja.2020-14
https://www.econstor.eu/bitstream/10419/219103/1/1699273340.pdf (application/pdf)

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Working Paper: A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012) (2020) Downloads
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