Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/33763 
Erscheinungsjahr: 
2006
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 2243
Verlag: 
Institute for the Study of Labor (IZA), Bonn
Zusammenfassung: 
The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.
Schlagwörter: 
cross section dependence
large panels , unit roots
principal components
common correlated effects
JEL: 
C12
C13
C33
Dokumentart: 
Working Paper

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