This chapter is concerned with methods for analyzing spatial data. After initial discussion of the nature of spatial data, including the concept of randomness, we focus most of our attention on linear regression models that involve interactions between agents across space. The introduction of...

This paper applies a non-parametric heteroscedasticity and autocorrelation consistent (HAC) estimator of error terms in the context of the spatial autoregressive model of GDP per capita convergence of European regions at NUTS 2 level. By introducing the spatial dimension, it looks how the...

We introduce a new approach for modelling risk dependence. The methods we describe are applicable to all risk types but are motivated by a need to robustly assess the dependence between operational and other risks such as market and credit.We show through a practical example how the technique...

This book develops spatial interaction models for the analysis of human interaction within space, in terms of both accessibility and land use. Presenting case studies on the Azores and Morocco, it covers applications in various regions of Europe and Africa. The respective models simulate land...