Modeling individual choices is one of the main aim in microeconometrics. Discrete choice models has been widely used to describe economic agents’ utility functions, and most of them play a paramount role in applied health economics. On the other hand, spatial econometrics collects a series of econometric tools which are particularly useful when we deal with spatially–distributed data sets. It has been demonstrated that accounting for spatial dependence can avoid inconsistency problems of the commonly used estimators. However, the complex structure of spatial dependence in most of the nonlinear models still precludes a large diffusion of these spatial techniques. The purpose of this paper is then twofold. The former is to review the main methodological problems and their different solutions in spatial discrete choice modeling as they have appeared in the econometric literature. The latter is to review their applications to health issues, especially in the last few years, by highlighting at least two main reasons why spatial discrete neighboring effects should be considered and then suggesting possible future lines of the development of this emerging field. Particular attention has been paid on cross–sectional spatial discrete choice modeling. However, discussions on the main methodological advancements in other spatial limited dependent variable models (like e.g. Tobit models) and spatial panel data models are also included.
|Numero di pagine||24|
|Rivista||Journal of Economic Surveys|
|Stato di pubblicazione||Pubblicato - 2019|
- nonlinear modeling, spatial econometrics, peer effects, health economics
- scelte discete, modeli spaziali