Inference in Semiparametric Binary Response Models with Interval Data
This paper studies the semiparametric binary response model with interval data investigated by Manski and Tamer (2002, MT). In this partially identified model, we propose a new estimator based on MT's modified maximum score (MMS) method by introducing density weights to the objective function, which allows us to develop asymptotic properties of the proposed set estimator for inference. We show that the density-weighted MMS estimator converges to the identified set at a nearly cube-root-n rate. Further, we propose an asymptotically valid inference procedure for the identified region based on subsampling. Monte Carlo experiments provide supports to our inference procedure.
|Date of creation:||25 Jun 2013|
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- Magnac, Thierry & Maurin, Eric, 2004.
"Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data,"
IDEI Working Papers
280, Institut d'Économie Industrielle (IDEI), Toulouse, revised Jan 2005.
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- Thierry Magnac & Eric Maurin, 2004. "Partial Identification in Monotone Binary Models : Discrete Regressors and Interval Data," Working Papers 2004-11, Centre de Recherche en Economie et Statistique.
- Thierry Magnac & Eric Maurin, 2008. "Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data," Post-Print halshs-00754272, HAL.
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