标题：Linear Social Network Models

汇报人：Steven Durlauf, University of Wisconsin

功夫：2:00-3:30pm, June 19th

地址：拉斯维加斯9888新楼216教室

Abstract:

This paper provides a systematic analysis of identification in linear social networks models. This is both a theoretical and an econometric exercise in that it links identification analysis to a rigorously delineated model of interdependent decisions. We develop a Bayes-Nash equilibrium analysis for interdependent decisions under incomplete information in networks that produces linear strategy profiles of the type conventionally used in empirical work and which nests linear social interactions models as a special case. We consider identification of both contextual and endogenous social effects under alternative assumptions on the a priori information on network structure available to an analyst and contrast the informational content of individual-level and aggregated data. This analysis is then extended to an example of a two stage game in which networks form in the first stage and outcomes occur in the second. The effects of endogenous network formation on identification are then analyzed.