期刊名称：Cambridge Working Papers in Economics / Faculty of Economics ; Department of Applied Economics
出版年度：2007
卷号：1
出版社：Cambridge University
摘要：This paper considers the statistical analysis of large panel data sets where even after conditioning on common observed e¤ects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common e¤ects and/or if there are spill over e¤ects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the concepts 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 Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, 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 matrix.
关键词：Panels, Common Correlated Effects, Strong and Weak Cross Section Dependence.
