Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/62910 
Year of Publication: 
2005
Series/Report no.: 
Working Paper No. 536
Publisher: 
Queen Mary University of London, Department of Economics, London
Abstract: 
This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004), and the other uses principal components following the work of Stock and Watson (2002). The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.
Subjects: 
Cross section dependence, Large panels, Principal components, Common correlated effects, Return equations
JEL: 
C12
C13
C33
Document Type: 
Working Paper

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