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.
Categories: stat.AP math.ST stat.TH