Most regional empirical analyses are limited by the lack of data. Researchers have to use information that is structured in administrative or political regions which are not always economically meaningful. The non-availability of geographically disaggregated information prevents to obtain...

The traditional approach to estimate spatial models bases on a preconceived spatial weights matrix to measure spatial interaction among locations. The a priori assumptions used to define this matrix are supposed to be in line with the "true" spatial relationships among the locations of the...

Most regional empirical analyses are limited by the lack of data. Researchers have to use information that is structured in administrative or political regions which are not always economically meaningful. The non-availability of geographically disaggregated information prevents to obtain...

<title>Abstract</title> In this paper we adopt an entropy econometrics-based estimator to study regional variations in regression coefficients and apply it to analyse productivity growths generated by R&D activities at a regional level. Considering the possible effects of the region's own R&D stock as well as...

A relatively frequent problem when cross-classified data is needed (for example region <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\times $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mo>×</mo> </math> </EquationSource> </InlineEquation> industry) is that only aggregate (not cross-classified) data exists. Filling the gaps by combining data from diverse sources usually requires data conciliation. Ecological inference and...</equationsource></equationsource></inlineequation>

Most regional empirical analyses are limited by the lack of data. Researchers have to use information that is structured in administrative or political regions which are not always economically meaningful. The non-availability of geographically disaggregated information prevents to obtain...