Partial identification of nonlinear peer effects models with missing data

This paper examines inference on social interactions models in the presence of missing data on outcomes. In these models, missing data on outcomes imply an incomplete data problem on both the endogenous variable and the regressors. However, getting a sharp estimate of the partially identified coefficients is computationally difficult. Using a monotonicity property of the peer effects and a mean independence condition of individual decisions on the missing data, I show partial identification results for the binary choice peer effect model. A Monte Carlo exercise then summarizes the computational time and the accuracy performance of the interval estimators under some calibrations.