The purpose of this thesis is to develop statistical tools to initialize and to calibrate dynamic stochastic microsimulation models, starting from their application to the SimVillages model (developed within the European PRIMA project). This model includes demographic and economic dynamics applied to the population of a set of rural municipalities. Each individual, represented explicitly in a household living in a municipality, possibly working in another, has its own life trajectory. Thus, model includes rules for the choice of study, career, marriage, birth children, divorce, migration, and death. We developed, implemented and tested the following models: * a model to generate a synthetic population from aggregate data, where each individual lives in a household in a municipality and has a status with regard to employment. The synthetic population is the initial state of the model. * a model to simulate a table of origin-destination commuting from aggregate data in order to assign a place of work for each individual working outside his municipality of residence. * a sub-model to estimate the number of jobs in local services in a given municipality in terms of its number of inhabitants and its neighbors in terms of service. * a method to calibrate the unknown SimVillages model parameters in order to satisfy a set of criteria. This method is based on a new Approximate Bayesian Computation algorithm using importance sampling. When applied to a toy example and to the SimVillages model, our algorithm is 2 to 8 times faster than the three main sequential ABC algorithms currently available.
Initialize and Calibrate a Dynamic Stochastic Microsimulation Model: Application to the SimVillages Model