Title: Dynamic probit models with network interdependence and unobserved heterogeneity
Authors: Michaela Kesina - University of Groningen (Netherlands) [presenting]
Peter Egger - ETH Zurich (Switzerland)
Abstract: A Bayesian estimation framework is proposed for panel-data sets with binary dependent variables where a large number of cross-sectional units is observed over a short period of time, and cross-sectional units are interdependent. Our estimation approach enables accounting for various forms of dynamic relationships and different types of cross-sectional dependence. These features should make the approach interesting for applications in many empirical contexts. The estimation approach is outlined. Its suitability is illustrated through simulation examples. An application is provided to study dynamic exporting patterns among Chinese firms.