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A Smoothed Maximum Score Estimator for the Binary Response Model
Abstract
This paper describes a semiparametric estimator for binary response models in which there may be arbitrary heteroskedasticity of unknown form. The estimator is obtained by maximizing a smoothed version of the objective function of C. Manski's maximum score estimator. The smoothing procedure is similar to that used in kernel nonparametric density estimation. The resulting estimator's rate of convergence in probability is the fastest possible under the assumptions that are made. The centered, normalized estimator is asymptotically normally distributed. Methods are given for consistently estimating the parameters of the limiting distribution and for selecting the bandwidth required by the smoothing procedure. Copyright 1992 by The Econometric Society.
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Citations
Cited by
(2001) · RePEc: Research Papers in Economics · Cited by 27,577
An Efficient Semiparametric Estimator for Binary Response Models
Klein, R. W., & Spady, R. H. (1993) · Econometrica · doi:10.2307/2951556 · Cited by 759
Panel Data Models: Some Recent Developments
Arellano, M., & Honoré, B. (2001) · Handbook of Econometrics · doi:10.1016/s1573-4412(01)05006-1 · Cited by 561
Estimation of semiparametric models when the criterion function is not smooth
Chen, X., Linton, O., & Van Keilegom, I. (2003) · Econometrica · doi:10.1111/1468-0262.00461 · Cited by 411
Metadata
- Authors
- Horowitz, J. L.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1992-05
- Volume
- 60
- Issue
- 3
- Pages
- 505
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2022050125
- Cited by
- 589
- References
- 0
- FWCI
- 10.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- Horowitz, J. L. (1992). A Smoothed Maximum Score Estimator for the Binary Response Model. Econometrica. https://doi.org/10.2307/2951582
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