Inference in Semiparametric Binary Response Models with Interval Data
AbstractThis 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.
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Bibliographic InfoPaper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-492.
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Date of creation: 25 Jun 2013
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Interval data; semiparametrc binary response model; density weights; U-process;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-05 (All new papers)
- NEP-ECM-2013-07-05 (Econometrics)
- NEP-ORE-2013-07-05 (Operations Research)
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