I show a simple back-of-the-envelope method for calculating marginal effects in binary choice and count data models. The approach suggested here focuses attention on marginal effects at different points in the distribution of the dependent variable rather than representative points in the joint...

In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is...

This paper explores the problems associated with the location choice of newly created establishments at the aggregate level. Much work has been done in this domain, however, several issues arise when analyzing involved phenomena, which scholars have yet to fully explore: 1) addressing the excess...