arXiv 4 Mar 2019 · Econometrics · publishedThe Review of Economic Studies (2025) · 4 citations (OpenAlex)
arXiv:1903.01511 · PDF · DOI · OpenAlex · Extracted main text
We provide a finite sample inference method for the structural parameters of a semiparametric binary response model under a conditional median restriction originally studied by Manski (1975, 1985). Our inference method is valid for any sample size and irrespective of whether the structural parameters are point identified or partially identified, for example due to the lack of a continuously distributed covariate with large support. Our inference approach exploits distributional properties of observable outcomes conditional on the observed sequence of exogenous variables. Moment inequalities conditional on this size n sequence of exogenous covariates are constructed, and the test statistic is a monotone function of violations of sample moment inequalities. The critical value used for inference is provided by the appropriate quantile of a known function of n independent Rademacher random variables. We investigate power properties of the underlying test and provide simulation studies to support the theoretical findings.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen and Lee (2019) Breaking the Curse of Dimensionality in Conditional Moment Inequalities for Discrete Choice Models | 0.874 | 5 | 2 | 100% |
| 2 | Manski (1985) Semiparametric Analysis of Discrete Response: Asymptotic Properties of the Maximum Score Estimator | 0.874 | 5 | 2 | 100% |
| 3 | Lehmann and Romano (2005) Testing Statistical Hypotheses, Third Edition | 0.822 | 6 | 2 | 83% |
| 4 | Horowitz (1992) A Smoothed Maximum Score Estimator for the Binary Response Model | 0.811 | 4 | 2 | 100% |
| 5 | Mukherjee, Banerjee, and Ritov (2019) Non-Standard Asymptotics in High Dimensions: Manski's Maximum Score Estimator Revisited | 0.644 | 2 | 2 | 100% |
| 6 | Gu and Koenker (2018) Nonparametric Maximum Likelihood Methods for Binary Response Models with Random Coefficients | 0.585 | 3 | 1 | 100% |
| 7 | Komarova (2013) Binary Choice Models with Discrete Regressors: Identification and Misspecification | 0.585 | 3 | 1 | 100% |
| 8 | Blevins (2015) Non-standard Rates of Convergence of Criterion-Function-Based Set Estimators for Binary Response Models | 0.585 | 3 | 1 | 100% |
| 9 | Horowitz (2002) Bootstrap Critical Values for Tests Based on the Smoothed Maximum Score Estimator | 0.585 | 3 | 1 | 100% |
| 10 | Rada and Cerný (2018) A New Algorithm for Enumeration of Cells of Hyperplane Arrangements and a Comparison with Avis and Fukuda's Reverse Search | 0.511 | 2 | 1 | 100% |
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