Esimating mixed spatial processes in information-theoretic frameworks

Abstract: - Information theoretic estimators do not require specification of the specific parametric functional form of sampling distributions or likelihood functions but it is only necessary to make mild assumptions concerning the existence of zero-valued moment conditions. This work aims at developing an entropy-based estimation strategy of a dynamic spatial heteroge-neous panel data model where separate processes for each unit are considered. An empirical application is provided to demonstrate practical implementation of the GME estimator when one has to deal with estimation of ill-posed or ill-conditioned models in analyzing spatial structures. Keywords: - Dynamic panel data, Generalized maximum entropy estimation, Spatial model

Estimating Spatial Interaction Models using Panel Data: a Generalized Maximum Entropy Formulation

Flow data are viewed as cross-classified data, and spatial interaction models are reformulated as log-linear models. According to this view, we introduce a spatial panel data model and we derive a Generalized Maximum Entropy \u2013 based estimation formulation. The estimator we propose has the advantage of being consistent with the underlying data generation process and eventually with the restrictions implied by some non sample information or by past empirical evidence by also controlling for collinearity and endogeneity problems