Estimating Dynamic Local Interactions Models

This paper presents empirical methods for studying a class of local interac-tions models in which agents ’ transitions are affected by their neighbors ’ states. We consider an application to urban unemployment and social networks in job search using publicly available cross-section and retrospective data. Most links in our model are local, but some span an entire metropolitan area. Our methods are designed to accommodate the presence of strong cross-sectional dependence arising from these few cross-metro area links. We also present simple methods to compare data and model spell distributions and to illustrate the model’s dynamic properties