A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models
University of Cergy-Pontoise - Department of Economics
Université Libre de Bruxelles (ULB) - European Center for Advanced Research in Economics and Statistics (ECARES); Centre for Economic Policy Research (CEPR)
London Business School; Université Libre de Bruxelles (ULB) - European Center for Advanced Research in Economics and Statistics (ECARES); Centre for Economic Policy Research (CEPR); European Central Bank (ECB)
ECB Working Paper No. 674
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).
Number of Pages in PDF File: 37
Keywords: Factor Model, large cross-sections, Quasi Maximum Likelihood
JEL Classification: C51, C32, C33working papers series
Date posted: September 12, 2006
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