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A consistent bootstrap procedure for the maximum score estimator

Rohit Kumar Patra, Emilio Seijo and Bodhisattva Sen

Journal of Econometrics, 2018, vol. 205, issue 2, 488-507

Abstract: In this paper we propose a new model-based smoothed bootstrap procedure for making inference on the maximum score estimator of Manski (1975, 1985) and prove its consistency. We provide a set of sufficient conditions for the consistency of any bootstrap procedure in this problem. We compare the finite sample performance of different bootstrap procedures through simulation studies. The results indicate that our proposed smoothed bootstrap outperforms other bootstrap schemes, including the m-out-of-n bootstrap. Additionally, we prove a convergence theorem for triangular arrays of random variables arising from binary choice models, which may be of independent interest.

Keywords: Binary choice model; Cube-root asymptotics; (In)-consistency of the bootstrap; Latent variable model; Smoothed bootstrap (search for similar items in EconPapers)
JEL-codes: C14 C25 (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:205:y:2018:i:2:p:488-507

DOI: 10.1016/j.jeconom.2018.04.001

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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