Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/324454 
Year of Publication: 
2025
Series/Report no.: 
cemmap working paper No. CWP16/25
Publisher: 
Centre for Microdata Methods and Practice (cemmap), The Institute for Fiscal Studies (IFS), London
Abstract: 
This paper presents a computationally efficient method for binary classification using Manski's (1975,1985) maximum score model when covariates are discretely distributed and parameters are partially but not point identified. We establish conditions under which it is minimax optimal to allow for either non-classification or random classification and derive finite-sample and asymptotic lower bounds on the probability of correct classification. We also describe an extension of our method to continuous covariates. Our approach avoids the computational difficulty of maximum score estimation by reformulating the problem as two linear programs. Compared to parametric and nonparametric methods, our method balances extrapolation ability with minimal distributional assumptions. Monte Carlo simulations and empirical applications demonstrate its effectiveness and practical relevance.
Subjects: 
Binary classification
maximum score estimation
partial identification
finite-sample and asymptotic inference
extrapolation
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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