  This paper develops a micro-founded peer effect model for count responses
using a game of incomplete information. The model incorporates heterogeneity in
peer effects through agents' groups based on observed characteristics.
Parameter identification is established using the identification condition of
linear models, which relies on the presence of friends' friends who are not
direct friends in the network. I show that this condition extends to a large
class of nonlinear models. The model parameters are estimated using the nested
pseudo-likelihood approach, controlling for network endogeneity. I present an
empirical application on students' participation in extracurricular activities.
I find that females are more responsive to their peers than males, whereas male
peers do not influence male students. An easy-to-use R packag--named
CDatanet--is available for implementing the model.
