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A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models

  • 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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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
Date of revision:
Publication status: Published by:
Handle: RePEc:eca:wpaper:2008_034
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  1. Sentana, E. & Shah, M., 1994. "An Index of Co-Movements in Financial Time Series," Papers 9415, Centro de Estudios Monetarios Y Financieros-.
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  11. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00638009, HAL.
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  17. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, 01.
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  29. Catherine Doz & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Post-Print peer-00844811, HAL.
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