Robust and Efficient Adaptive Estimation of Binary-Choice Regression Models

Pavel Cizek

No 2007-12, Discussion Paper from Tilburg University, Center for Economic Research

Abstract: The binary-choice regression models such as probit and logit are used to describe the effect of explanatory variables on a binary response vari- able. Typically estimated by the maximum likelihood method, estimates are very sensitive to deviations from a model, such as heteroscedastic- ity and data contamination. At the same time, the traditional robust (high-breakdown point) methods such as the maximum trimmed like- lihood are not applicable since, by trimming observations, they induce the separation of data and non-identification of parameter estimates. To provide a robust estimation method for binary-choice regression, we con- sider a maximum symmetrically-trimmed likelihood estimator (MSTLE) and design a parameter-free adaptive procedure for choosing the amount of trimming. The proposed adaptive MSTLE preserves the robust prop- erties of the original MSTLE, significantly improves the infinite-sample behavior of MSTLE, and additionally, ensures asymptotic efficiency of the estimator under no contamination. The results concerning the trim- ming identification, robust properties, and asymptotic distribution of the proposed method are accompanied by simulation experiments and an application documenting the infinite-sample behavior of some existing and the proposed methods.

JEL-codes: C13 C20 C21 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm and nep-ecm
Date: 2007
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Journal Article: Robust and Efficient Adaptive Estimation of Binary-Choice Regression Models (2008)
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