LFU:online

An introduction to applied spatial econometrics

Suggested abstract:
Spatial processes and autocorrelation:
The first half of the course will discuss spatial processes in applied contexts. In situations in which values of a variable of interest can be predicted from its near neighbours, the assumption of independence of observations is not sustained. The presence of spatial processes expressed as spatial autocorrelation may be used to enhance models. However, their presence also affects inference in models which do not take them into account. There are a number of ways to represent relationships between observations, expressing approximations to unobserved spatial processes.These may be used for testing for spatial autocorrelation, but such tests assume that our understanding of the data generation process is adequate, without omitted covariates or inappropriate functional forms.

Modelling spatial data:
The second half of the course will survey the fitting of spatial regression models for continuous and discrete response variables, as applied in spatial econometrics, political science, and other disciplines. It will be pointed out that, in applied work, the best model may be one in which no residual spatial process is found; the parsimonious model may be one in which the correctly specified model has no “spatial story” of spillovers or other unobserved causal factors. However, on occasion, spatial processes are helpful in modelling, sometimes because it is not possible to observe the covariates that are proxied by relationships between neighbouring observations.