Finite Sample Inference for the Maximum Score Estimand

Adam Rosen and Takuya Ura

The Review of Economic Studies, 2025, vol. 92, issue 6, 4117-4151

Abstract: We provide a finite sample inference method for the structural parameters of Manski's semiparametric binary response model under a conditional median restriction. This is achieved by exploiting distributional properties of observable outcomes conditional on the observed sequence of exogenous variables. Moment inequalities conditional on the size n sequence of exogenous covariates are constructed, and the proposed test statistic is a monotone function of violations of the corresponding sample moment inequalities. The critical value used for inference is provided by the appropriate quantile of a known function of n independent Bernoulli random variables and does not require the use of a cube root asymptotic approximation employing a point estimator of the target parameter. Simulation studies demonstrate favourable finite sample performance of the test in comparison to several existing approaches. Empirical use is illustrated with an application to the classical setting of transportation choice.

Keywords: Finite sample inference; Maximum score estimation; Moment inequalities; Partial identification (search for similar items in EconPapers)
Date: 2025
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Working Paper: Finite Sample Inference for the Maximum Score Estimand (2020)
Working Paper: Finite Sample Inference for the Maximum Score Estimand (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:restud:v:92:y:2025:i:6:p:4117-4151.

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