Introduction Panel data-sets have been increasingly used in economics to analyse complex economic phenomena. One of their attractions is the ability to use an extended data-set to obtain information about parameters of interest which are assumed to have common values across panel units. Most of the work carried out on panel data has usually assumed some form of cross-sectional independence to derive the theoretical properties of various inferential procedures. However, such assumptions are often suspect and as a result recent advances in the literature have focused on estimation of panel data models subject to error cross-sectional dependence. A number of different approaches have been advanced for this purpose. In the case of spatial data-sets where a natural immutable distance measure is available the dependence is often captured through “spatial lags” using techniques familiar from the time series literature. In economic applications, spatial techniques are often adapted using alternative measures of “economic distance”. This approach is exemplified in work by Lee and Pesaran (1993), Conley and Dupor (2003), Conley and Topa (2002) and Pesaran, Schuermann, and Weiner (2004), as well as the literature on spatial econometrics recently surveyed by Anselin (2001). In the case of panel data models where the cross-section dimension ( N ) is small (typically N < 10) and the time-series dimension ( T ) is large the standard approach is to treat the equations from the different cross-section units as a system of seemingly unrelated regression equations (SURE) and then estimate the system by the Generalized Least Squares (GLS) techniques.
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