 Modelling urban poverty in the Montreal metropolitan region between 1986 and 2016 
There is a growing interest for spatial econometric models in geography. Recently, extensions of the cross-sectional spatial autoregressive models have been proposed to consider the temporal dimension of panel data (N locations at T time periods). Currently, these models are still little known to geographers. Therefore, the aim of this paper is to describe these models and present their advantages in analyzing the linear relation between a variable of interest and explanatory variables in the case of urban panel data. We first summarize panel models and their specifications. Then, we describe a methodological routine to identify the most appropriate model for a spatial panel dataset among various spatial panel models. Our empirical application of this method on poverty in the region of Montreal (at census tract level) from 1986 to 2016 shows its robustness to control for spatial dependency, but also its ability to identify several spatial effects ignored by classical aspatial models (spillover effects of the dependent and independent variables and feedback effect).