Count Data Models with Heterogeneous Peer Effects under Rational Expectations
Identifying heterogeneous peer effects in count-response data remains challenging due to unobserved heterogeneity and network endogeneity. Method: This paper develops a rational-expectations game-theoretic model under incomplete information to characterize individual decision-making under heterogeneous peer influence in social networks. It extends linear identification conditions to generalized nonlinear count models for the first time; proposes a novel network-exogeneity identification strategy leveraging “friends-of-friends” to jointly identify network endogeneity and heterogeneous peer effects; and introduces a nested pseudo-likelihood (NPL) estimator, implemented in the open-source R package CDatanet for empirical replication. Contribution/Results: Empirical analysis reveals significant same-gender peer effects on girls’ extracurricular participation, whereas boys’ participation is unaffected by same-gender peers—highlighting gender-differentiated social influence mechanisms in adolescent behavior.