Estimating dynamic local interactions models

This paper presents empirical methods for studying a class of local interactions 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.