Epidemiological studies are largely purposed to provide outcomes that may be used for interventions and development programs. In recent years, geo-referencing of epidemiological data has become one of the vital features. Often, epidemiological data collected for regions under study will show...

Abstract We consider the problem of maximizing the utility of consumption and terminal wealth in a geometric Ornstein–Uhlenbeck market. We calculate the optimal consumption and wealth processes for power, logarithmic and exponential utility as well as their behavior depending e.g. on...

In this article, we analyze competition for agricultural land as an important, scarce and immobile input. The cost of cultivating a parcel of land depends strongly on the distance from the farmer to the plot, leading to spatially small land markets. To investigate this issue, we are able to use...

Nighttime lights observed from satellites are a widely accepted proxy measure for economic development. This is mainly based on cross-country evidence that finds strong correlations between lights and Gross Domestic Product. Yet, the evidence on the correlations at local levels is scarce, and it...

Composite spatial data on administrative area level are often presented by maps. The aim is to detect regional differences in the concentration of subpopulations, like elderly persons, ethnic minorities, low-educated persons, voters of a political party or persons with a certain disease....

We use consumer price data for 205 cities/regions in 21 countries to study PPP deviations before, during and after the major currency crises of the 1990s. We combine data from industrialized nations in North America (Unites States, Canada and Mexico), Europe (Germany, Italy, Spain and Portugal),...

Central limit theorems are developed for instrumental variables estimates of linear and semi-parametric partly linear regression models for spatial data. General forms of spatial dependenceand heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss...

Nonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects spatial correlation. Conditional heteroscedasticity is also allowed, as well...

Researchers are increasingly able to capture spatially referenced data on both a response and a covariate more frequently and in more detail. A combination of geostatisical models and analysis of covariance methods may be used to analyze such data. However, very basic questions regarding the...