Abstract
This article provides symbolic analysis tools for specifying spatial econometric models. It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly considers how to econometrically model spatial economic relations that might contain unobserved spatial structure of unknown form. Hypothesis testing is conducted with a symbolic-entropy based non-parametric statistical procedure, recently proposed by Garcia-Cordoba, Matilla-Garcia, and Ruiz (2019), which does not rely on prior weight matrices assumptions. It is shown that the use of geographically restricted semiparametric spatial models is a promising modeling strategy for cross-sectional datasets that are compatible with some types of spatial dependence. The results state that models that merely incorporate space coordinates might be sufficient to capture space dependence. Hedonic models for Baltimore, Boston, and Toledo housing prices datasets are revisited, studied (with the new proposed procedures), and compared with standard spatial econometric methodologies.
Highlights
Spatial trends have not played a prominent role in explaining and understanding how the outcomes in one geographical location are related to the outcomes in nearby locations
In the case of a true spatial dependence via W, the delta-test will tend to point that no spatial trend is found in the residuals, and the researcher will have to deal with a statistically correct specification of the model
The article contributes, on the one hand, to the use of specification tests in order to assess the robustness of the results, once it is recognized that our ability to accurately model spatial data is very limited
Summary
Spatial trends have not played a prominent role in explaining and understanding how the outcomes in one geographical location are related to the outcomes in nearby locations (regions, countries, or points in space). This is especially evident in the spatial econometric literature. This absence contrasts with the role that time trends have played in time series econometrics for explaining economic outcomes that are close in temporal terms. This paper aims to deal with the use of spatial trends when modeling and studying economic relations where directly or indirectly there are spatially correlated missing variables. Spatial spillovers and geographically clustered errors have been at the core of the development of spatial econometrics
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