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Title: A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models

Author(s): Catherine Doz, Domenico Giannone and Lucrezia Reichlin

Publication Date: June 2006

Keyword(s): factor model, large cross-sections and Quasi Maximum Likelihood

Programme Area(s): International Macroeconomics

Abstract: This paper considers quasi-maximum likelihood estimations of a dynamic approximate factor model when the panel of time series is large. Maximum likelihood is analyzed under different sources of misspecification: omitted serial correlation of the observations and cross-sectional correlation of the idiosyncratic components. It is shown that the effects of misspecification on the estimation of the common factors is negligible for large sample size (T) and the cross-sectional dimension (n). The estimator is feasible when n is large and easily implementable using the Kalman smoother and the EM algorithm as in traditional factor analysis. Simulation results illustrate what are the empirical conditions in which we can expect improvement with respect to simple principle components considered by Bai (2003), Bai and Ng (2002), Forni, Hallin, Lippi, and Reichlin (2000, 2005b), Stock and Watson (2002a,b).

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Bibliographic Reference

Doz, C, Giannone, D and Reichlin, L. 2006. 'A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models'. London, Centre for Economic Policy Research.