Pairwise influences in dynamic choice: network-based model and application.

In this paper, we study the problem of network discovery and influence propagation, and propose an integrated approach for the analysis of lead-lag synchronization in multiple choices. Network models for the processes by which decisions propagate through social interaction have been studied before, but only a few consider unknown structures of interacting agents. In fact, while individual choices are typically observed, inferring individual influences - who influences who - from sequences of dynamic choices requires strong modeling assumptions on the cross-section dependencies of the observed panels. We propose a class of parametric models which extends the vector autoregression to the case of pairwise influences between individual choices over multiple items and supports the analysis of influence propagation. After uncovering a collection of theoretical properties (conditional moments, parameter sensitivity, identifiability and estimation), we provide an economic application to music broadcasting, where a set of songs are diffused over radio stations; we infer station-to-station influences based on the proposed methodology and assess the propagation effect of initial launching stations to maximize songs diffusion. Both on the theoretical and empirical sides, the proposed approach connects fields which are traditionally treated as separated areas: the problem of network discovery and the one of influence propagation.