Model Uncertainty in Matrix Exponential Spatial Growth Regression Models

This article considers the most important aspects of model uncertainty for spatial regression models, namely, the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation, and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity‐based, k‐nearest neighbor, and distance‐based spatial weight matrices), using a data set of income per capita growth for 273 European regions.     

Alternative Zuliefersysteme im Brauereisektor – Regionalmodelle des Braugerstenbezugs in Bayern

Standort - Seit Jahrzehnten wird Bier größtenteils in standardisierten Wertschöpfungsketten produziert, wobei die Kontexte der Rohstofferzeugung kaum mehr nachvollziehbar erscheinen....     

Human capital accumulation and long‐term income growth projections for European regions

We propose an econometric framework to construct projections for per capita income growth and human capital for European regions. Using Bayesian methods, our approach accounts for model uncertainty in terms of the choice of explanatory variables, the nature of spatial spillovers, as well as the potential endogeneity between output growth and human capital accumulation. This method allows us to assess the potential contribution of future educational attainment to economic growth and income convergence among European regions over the next decades. Our findings suggest that income convergence dynamics and human capital act as important drivers of income growth for the decades to come.     

Water availability and landuse during the Upper and Epipaleolithic in southwestern Syria

Paleolithic research often assumes that environmental conditions played a major role in shaping human evolution. To study this relationship we present a spatially explicit approach based on the assumption that the distribution of water within the landscape is an essential component of local environmental conditions. Here, we analyze the relation of wetness and human landuse patterns from the Upper Paleolithic (UP) and Epipaleolithic (EP) of Western Syria. In particular the spatially explicit character of the approach enables the detection of a significant change in landuse patterns during the UP and EP accompanied by a significant shift in the wetness characteristics of the preferentially used areas. These results are discussed against the background of published data on climatic conditions in order to identify both a possible time frame and triggers for this change in landuse. While we conclude an increased influence of natural conditions on the spatial behavior for the UP, we suggest an additional influence of cultural circumstances in shaping EP spatial behavior. For the region studied we argue that the bounded pattern observed during the UP changes to a spatially flexible pattern during the Late Natufian.