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• W2123266275 abstract "This paper considers the statistical analysis of large panel data sets where even after condi- tioning on common observed eects the cross section units might remain dependently distrib- uted. This could arise when the cross section units are subject to unobserved common eects and/or if there are spill over eects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the con- cepts of time-speci…c weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Eects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coe¢ cient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight" @default.
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• W2123266275 date "2011-04-01" @default.
• W2123266275 modified "2023-10-04" @default.
• W2123266275 title "Large panels with common factors and spatial correlation" @default.
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• W2123266275 doi "https://doi.org/10.1016/j.jeconom.2010.12.003" @default.
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