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This study focuses on integration processes in European Research and Development ( R&D) by analyzing the spatiotemporal dimension of three different R&D collaboration networks across Europe. The studied networks cover different types of knowledge creation, namely project-based R&D networks within the European Union ( EU) Framework Programmes ( FPs), co-patent networks, and co-publication networks. Integration in European R&D-one of the main pillars of the EU Science Technology and Innovation policy-refers to the harmonization of fragmented national research systems across Europe and to the free movement of knowledge and researchers. The objective is to describe and compare spatiotemporal patterns at a regional level and to estimate the evolution of separation effects over the time period 1999-2006 that influence the probability of cross-region collaborations in the distinct networks under consideration. The study adopts a spatial interaction modeling perspective, econometrically specifying a panel generalized linear model relationship, taking into account spatial autocorrelation among flows using eigenfunction spatial filtering methods. The results show that geographical factors are a lower hurdle for R&D collaborations in the FP networks than in co-patent networks and co-publication networks. Furthermore, it is shown that the geographical integration is higher in the FP network. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the problems caused by the existence of instability in cross-sectional regressions. The results about local autocorrelation measures are part of this debate, as are the proposals concerning the concept of geographically weighted regressions. This article also deals with the problem of stability (or the lack thereof), but focusing the discussion on the supposition of constancy in the parameter of spatial dependence. In most cases, this assumption is treated, with the risks that this involves, as a maintained hypothesis, which should be ascertained before continuing with the modeling exercise. In the article, we present a simple heterogeneity test for this type of parameters, based on the Lagrange Multiplier principle. To illustrate its use, we take the distribution of per capita income among the European regions as our discussion case. According to our results, there are clear signs of structural breaks in the spatial distribution of this variable and the scale factor and the autocorrelation coefficient appear to be principal actors. [ABSTRACT FROM AUTHOR]