Identifying nonlinear spatial dependence patterns by using non-parametric tests: Evidence for the European Union

Accounting for spatial structures in econometric studies is becomingan issue of special interest, given the presence of spatial dependence and spatialheterogeneity problems arising in data. Generally, researchers have been employingparametric tests for detecting spatial dependence structures: Moran’s I and LM testsin spatial regressions are the most popular approaches employed in literature.However,this approach remains misleading in the presence of nonlinear spatial structures,inducing important biases in the estimation of the parameters of the model.In this paper we illustrate that issue by applying three non-parametrical proposalswhen testing for spatial structure in data. Empirical findings for the regions of theEuropean Union show important failures of traditional parametric tests if nonlinearitiescharacterise geo-referenced data. Our results clearly recommend employing newfamilies of tests, beyond parametrical ones, when working in such environments.