Improving The Efficiency And Robustness Of The Smoothed Maximum Score Estimator
AbstractThe binary-response maximum score (MS) estimator is a robust estimator, which can accommodate heteroskedasticity of an unknown form; J. Horowitz (1992) defined a smoothed maximum score estimator SMS) and demonstrated that this improves the convergence rate for sufficiently smooth conditional error densities. In this paper we relax Horowitz’s smoothness assumptions of the model and extend his asymptotic results. We also derive a joint limiting distribution of estimators with different bandwidths and smoothing kernels. We construct an estimator that combines SMS estimators for different bandwidths and kernels to overcome the uncertainty over choice of bandwidth when the degree of smoothnes of error distribution is unknown. A Monte Carlo study demonstrates the gains in efficiency and robustness.
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Bibliographic InfoPaper provided by McGill University, Department of Economics in its series Departmental Working Papers with number 2004-01.
Length: 34 pages
Date of creation: Sep 2006
Date of revision:
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-09-23 (All new papers)
- NEP-DCM-2006-09-23 (Discrete Choice Models)
- NEP-ECM-2006-09-23 (Econometrics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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