This volume focuses on econometric models that confront estimation and inference issues occurring when sample data exhibit spatial or spatiotemporal dependence. This can arise when decisions or transactions of economic agents are related to the behaviour of nearby agents. Dependence of one...

There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based...

We examine the distribution of sales for a retail chain in the Houston market using a spatial gravity model. Unlike previous empirical studies, our approach models spatial dependencies among both consumers and retailers. The results show that both forms of spatial dependence exert statistically...

Atmospheric CO2 been growing at an increasing rate for many years and this suggests that investments may face an increasing rate of future disaster risk. We provide a simple variation of the Gordon Growth model that accounts for potential increasing disaster risks and provides a closedform bound...

House price regression residuals often display spatial dependence but historically mortgage models, which employ house prices, assume independence and use only the own borrower/loan characteristics. This manuscript uses a spatial probit model to investigate spatial dependence among the...

Using New Orleans foreclosure data, where each property has three appraisals (borrower, lender, and referee), we are able to investigate the factors affecting appraisal bias and accuracy for distressed properties. Unconditional analysis shows that on average lender appraisals are higher than...