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A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models

Listed author(s):
  • Catherine Doz
  • Domenico Giannone
  • Lucrezia Reichlin

Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T) going to infinity along any path of n and T and that therefore maximum likelihood is viable for n large. The estimator is robust to misspecification of the cross-sectional and time series correlation of the the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis.

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File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/54096/1/RePEc_eca_wpaper_2008_034.pdf
File Function: RePEc_eca_wpaper_2008_034
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Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number 2008_034.

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Length: 28 p.
Date of creation: 2008
Publication status: Published by:
Handle: RePEc:eca:wpaper:2008_034
Contact details of provider: Postal:
Av. F.D., Roosevelt, 39, 1050 Bruxelles

Phone: (32 2) 650 30 75
Fax: (32 2) 650 44 75
Web page: http://difusion.ulb.ac.be

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  1. Bańbura, Marta & Modugno, Michele, 2010. "Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data," Working Paper Series 1189, European Central Bank.
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  3. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2007. "A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering," CEPR Discussion Papers 6043, C.E.P.R. Discussion Papers.
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