We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices,...

Estimators of spatial autoregressive (SAR) models depend in a highly non-linear way on the spatial correlation parameter and least squares (LS) estimators cannot be computed in closed form. We first compare two simple LS estimators by distance and covariance properties and then we study the...

We introduce a new methodology to detect and measure economic activity using geospatial data and apply it to steel production, a major industrial pollution source worldwide. Combining plant output data with geospatial data, such as ambient air pollutants, nighttime lights, and temperature, we...

A common problem in historical research involves harmonizing geographic units across time or different levels of aggregation. One approach is to use " crosswalks" that associate factors located within one geographic unit to different " reference" units based on relative areas . We develop an...

In applied historical research, geographic units often differ in level of aggregation across datasets. One solution is to use crosswalks that associate factors located within one geographic unit to another, based on their relative areas. We develop an alternative approach based on relative...

Africa is estimated to have great potential for agricultural production, but there are a number of constraints inhibiting the development of that potential. Spatial data are increasingly important in the realization of potential as well as the associated constraints. With crop production data...