New spatial econometric techniques and applications in regional science

The papers appearing in this special issue of Papers in Regional Science, which is devoted to spatial econometrics, come from the First International Conference of the Spatial Econometrics Association held in Cambridge (UK) 12-14 July 2008.This conference was the first official meeting of the new association, which was established in May 2006 in Rome and which has already attracted more than 150 members from around the world.At the Cambridge conference there were close to 120 delegates and more than 100 papers were presented.With regard to the eight papers appearing in this special issue, we would particularly like to thank the authors and the referees for their contribution to what we believe is an interesting and lively selection.Recent years have seen a real explosion in the application of spatial statistical models in all branches of social sciences and in particular in economics.Spatial econometrics models have been used to analyse different topics (see for example Anselin et al. 2004 for a review) and as a matter of fact spatial regression techniques are now becoming an established component in the applied econometrics toolbox, as witnessed by the increasing attention given to this topic in standard econometrics textbooks (Maddala 2001;Woolridge 2002; Gujarati 2003;Kennedy 2003; Baltagi 2008).Of the eight papers published in this issue, three have a major methodological emphasis and the remaining five have a predominantly applied perspective.The methodological papers are concerned with cross-sectional spatial model estimation techniques (Fingleton and Le Gallo), space-time model comparisons (Griffith) and spatial concentration (Bavaud).The other papers touch very diverse applied topics and provide a good insight into the wide variety of subjects that can benefit from a spatial econometrics approach.They range from regional convergence of the per-capita income (Olejnik), land price modelling (Tsutsumi and Seya), and spatial income and educational inequalities (Tselios), to modelling political trends (Santolini) and industrial specialization (Ciriaci and Palma).In the remainder of this introduction we will provide a short summary of these various contributions.At the heart of spatial econometrics modelling, it is certainly the case that the various spatial regression models originating from the theory of random fields hold a prominent position (see Arbia 2006, for an account of these statistical foundations).By this we mean the spatial error model and the spatial lag model, which exploit autoregression in the error or in the dependent variable respectively; these two models are by far the most widely used in the spatial econometrics literature.One important issue regarding the estimation of these models is the fact that