  In this paper we study the applicability of the bootstrap to do inference on
Manski's maximum score estimator under the full generality of the model. We
propose three new, model-based bootstrap procedures for this problem and show
their consistency. Simulation experiments are carried out to evaluate their
performance and to compare them with subsampling methods. Additionally, we
prove a uniform convergence theorem for triangular arrays of random variables
coming from binary choice models, which may be of independent interest.
