Spatial panels are a powerful econometric tool for the estimation of spacedependent cross-sectional time-series models of economic phenomena. A plethora of parameters and possible specifications require a systematic approach to estimation. This paper presents a strategy of estimation to be considered in applied research on economic policy, including the concept of spatial spillovers and its local and global effects, direct and indirect impacts, as well as the role of different spatial weighting schemes. The paper presents fiscal factors affecting GDP between the years 2002-2015 in a number of European economies.

This paper is a methodological guide to using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data, and supervised learning, which displaces classical spatial econometrics. It shows the potential of using this developing methodology, as well as its pitfalls. It catalogues and comments on the usage of spatial clustering methods (for locations and values, both separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling and density indicators. It provides details of spatial machine learning models, which are combined with spatial data integration, modelling, model fine-tuning and predictions to deal with spatial autocorrelation and big data. The paper delineates “already available” and “forthcoming” methods and gives inspiration for transplanting modern quantitative methods from other thematic areas to research in regional science.

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The paper deals with the statistical modeling of convergence and cohesion over time with the use of kurtosis, skewness and L-moments. Changes in the shape of the distribution related to the spatial allocation of socio-economic phenomena are considered as an evidence of global shift, divergence or convergence. Cross-sectional time-series statistical modeling of variables of interest is to overpass the minors of econometric theoretical models of convergence and cohesion determinants. L-moments perform much more stable and interpretable than classical measures. Empirical evidence of panel data proves that one pure pattern (global shift, polarization or cohesion) rarely exists and joint analysis is required.