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A quasi maximum likelihood approach for large approximate dynamic factor models

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  • Doz, Catherine
  • Giannone, Domenico
  • Reichlin, Lucrezia

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). JEL Classification: C51, C32, C33

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 0674.

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Date of creation: Sep 2006
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Handle: RePEc:ecb:ecbwps:20060674

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Keywords: factor model; large cross-sections; Quasi Maximum Likelihood;

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  1. Domenico Giannone & Lucrezia Reichlin & David Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
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