Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153108
Authors: 
Doz, Catherine
Giannone, Domenico
Reichlin, Lucrezia
Year of Publication: 
2006
Series/Report no.: 
ECB Working Paper 674
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).
Subjects: 
factor model
large cross-sections
Quasi Maximum Likelihood
JEL: 
C51
C32
C33
Document Type: 
Working Paper
Social Media Mentions:

Files in This Item:
File
Size
590.94 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.