Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns

George Kapetanios and Mohammad Pesaran

No 536, Working Papers from Queen Mary University of London, School of Economics and Finance

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.

Keywords: Cross section dependence; Large panels; Principal components; Common correlated effects; Return equations (search for similar items in EconPapers)
JEL-codes: C12 C13 C33 (search for similar items in EconPapers)
Date: 2005-05-01
References: View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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Related works:
Working Paper: Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns (2005)
Working Paper: Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns (2005)
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