Abstract
This study considers the estimation of simultaneous binary choice models with social interactions, with particular focus on a bivariate case. Assuming that individuals make binary decisions simultaneously to maximize their overall utility, defined as the sum of utilities from each binary outcome, under incomplete information, we provide a sufficient condition for unique equilibrium, and develop a copula-based maximum likelihood estimator based on the nested fixed-point algorithm. As an empirical analysis, we apply the proposed model and method to National Longitudinal Study of Adolescent Health (Add Health) data in order to investigate the determinants of adolescents’ smoking and drinking behaviors. Our estimation results suggest that smoking and drinking are complementary activities, and that endogenous peer effects are significantly present for smoking behavior, but not for drinking.
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