Two-step centered spatio-temporal auto-logistic regression model

International audienceIn our study, we focus on spatio-temporal causal auto-logistic model and proposed a two-step-centered parametrization version of it. We study the existence of the joint law according to the conditional marginals. The simulation study show that the one-step model can not reflect the temporal data structure when both spatial and temporal dependance are strong, while for the two-step model, there is an adequate agreement between the data structure and the temporal large-scale structure. The results of estimation for simulated lattices over years were performed by expectation-maximization (EM) pseudo-likelihood in two stages. They show that under the two-step centered parametrization, the inference for parameters of both temporal and spatial regressions are accurate, while under one-step centered parametrization their inference are always conflicting

Testing for Asymmetries and Anisotropies in Regional Economic Models

This paper develops a new methodology for estimating and testing the form of anisotropy of homogeneous spatial processes. We derive a generalised version of the isotropy test proposed by Arbia, Bee and Espa (2013) and analyse its properties in various settings. In light of this, we propose a new approach that allows one to estimate and test under mild conditions any form of anisotropy in homogeneous spatial processes. The power of the test is studied by means of Monte Carlo simulations performed both on regularly and irregularly spaced data. Finally, the method is used to analyse the soybeans yields in the US