A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models

37 Pages Posted: 12 Sep 2006

See all articles by Catherine Doz

Catherine Doz

University of Cergy-Pontoise - Department of Economics

Domenico Giannone

Federal Reserve Banks - Federal Reserve Bank of New York; Centre for Economic Policy Research (CEPR)

Lucrezia Reichlin

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)

Multiple version iconThere are 2 versions of this paper

Date Written: September 2006

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).

Keywords: Factor Model, large cross-sections, Quasi Maximum Likelihood

JEL Classification: C51, C32, C33

Suggested Citation

Doz, Catherine and Giannone, Domenico and Reichlin, Lucrezia, A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models (September 2006). ECB Working Paper No. 674. Available at SSRN: https://ssrn.com/abstract=927425

Catherine Doz (Contact Author)

University of Cergy-Pontoise - Department of Economics ( email )

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Domenico Giannone

Federal Reserve Banks - Federal Reserve Bank of New York ( email )

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Centre for Economic Policy Research (CEPR)

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Lucrezia Reichlin

London Business School ( email )

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Université Libre de Bruxelles (ULB) - European Center for Advanced Research in Economics and Statistics (ECARES) ( email )

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Centre for Economic Policy Research (CEPR)

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European Central Bank (ECB) ( email )

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