Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia

This paper analyses a second-order polynomial spatial structure in the residues of a regression model. We propose a new specification that captures spatial dependence on two different levels, adding a new autoregressive cycle to the errors of the classical spatial error model (SEM). The inference problems of the parameters are solved by means of maximum likelihood estimation. The model is confirmed to identify two spatial structures of spatial dependence, global and local, by an empirical application in the analysis of municipal unemployment in the Spanish region of Andalusia. Finally, Monte Carlo is implemented to evaluate the performance of this strategy in a context of finite size samples.