A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models
Domenico Giannone () and
Lucrezia Reichlin ()
No 2008_034, Working Papers ECARES from ULB -- Universite Libre de Bruxelles
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.
Keywords: Factor Model; large cross-sections; Quasi Maximum Likelihood (search for similar items in EconPapers)
JEL-codes: C51 C32 C33 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models (2012)
Working Paper: A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models (2012)
Working Paper: A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models (2006)
Working Paper: A quasi maximum likelihood approach for large approximate dynamic factor models (2006)
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