This note implements a novel approach to formalizing spatial externalities by employing spatial econometric methods that combine spatial dependence in the form of spatial autoregressive processes and spatial heterogeneity in the form of spatial regimes. The results confirm earlier findings that...

This note implements a novel approach to formalizing spatial externalities by employing spatial econometric methods that combine spatial dependence in the form of spatial autoregressive processes and spatial heterogeneity in the form of spatial regimes. The results confirm earlier findings that...

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal...

We apply a Bayesian hierarchical Poisson spatial interaction model to the paper trail left by patent citations between high-technology patents in Europe to identify and measure spatial separation effects of interregional knowledge flows. The model introduced here is novel in that it allows for...

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal...