The Aggregated Spatial Logit Model
In problems of spatial choice, the choice set is often more aggregated than the one considered by decision-makers, typically because choice data are available only at the aggregate level. These aggregate choice units will exhibit heterogeneity in utility and in size. To be consistent with utility maximization, a choice model must estimate choice probabilities on the basis of the maximum utility within heterogeneous aggregates. The ordinary multinomial logit model (OMNL) applied to aggregate choice unit fails this criterion as it is estimated on the basis of average utility. In this thesis, the aggregated spatial logit model, which utilizes the theory underlying the nested logit model to estimate the appropriate maximum utilities of aggregates, is derived and discussed. Initially, the theoretical basis for the model is made clear and an asymptotic version of the model is derived. Secondly, the model is tested in a stimulated environment to demonstrate that the OMNL model lacks the generality of the aggregated model in the prescence of the heterogenous aggregates. Thirdly, full endogenous estimation of the aggregated model is studied with a view toward finding the best optimization algorithm. Finally, with all the elements in place, the model is tested in an application of migration from the Canadian Atlantic Provinces.