Until recently, considerable effort has been devoted to the estimation of panel data regression models without adequate attention being paid to the drivers of interaction amongst cross-section and spatial units. We discuss some new methodologies in this emerging area and demonstrate their use in measurement and inferences on cross-section and spatial interactions. Specifically, we highlight the important distinction between spatial dependence driven by unobserved common factors and those based on a spatial weights matrix. We argue that purely factor-driven models of spatial dependence may be inadequate because of their connection with the exchangeability assumption. The three methods considered are appropriate for different asymptotic settings; estimation under structural constraints when N is fixed and T -> 8, whilst the methods based on GMM and common correlated effects are appropriate when T » N -> 8. Limitations and potential enhancements of the existing methods are discussed, and several directions for new research are highlighted. © 2010 Springer-Verlag.
- Cross-sectional and spatial dependence
- spatial weights matrix
- spatial interactions
- monetary policy committee
- generalised method of moments