Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions.We show that the recently proposed methods by Xia et al.(2002) can be made robust in such a way that preserves all advantages of the original...

High breakdown-point regression estimators protect against large errors and data contamination. We adapt and generalize the concept of trimming used by many of these robust estimators so that it can be employed in the context of the generalized method of moments. The proposed generalized method...

High breakdown-point regression estimators protect against large errors and data con- tamination. We generalize the concept of trimming used by many of these robust estima- tors, such as the least trimmed squares and maximum trimmed likelihood, and propose a general trimmed estimator, which...

In the empirical literature on labour supply, several models are developed to incorporate constraints on working hours. These models do not address the question to which extent working hours are constrained within and between jobs. In this paper I investigate the effect of individual changes in...

In econometrics, as a rule, the same data set is used to select the model and, conditional on the selected model, to forecast.However, one typically reports the properties of the (conditional) forecast, ignoring the fact that its properties are affected by the model selection (pretesting).This...

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...

High breakdown-point regression estimators protect against large errors and data con- tamination. Motivated by some { the least trimmed squares and maximum trimmed like- lihood estimators { we propose a general trimmed estimator, which unifies and extends many existing robust procedures. We...

This paper introduces a new class of robust regression estimators. The proposed twostep least weighted squares (2S-LWS) estimator employs data-adaptive weights determined from the empirical distribution, quantile, or density functions of regression residuals obtained from an initial robust fit....

We take a fresh look at Theil's BLUS residuals and ask why they have gone out of fashion.All our simulation experiments indicate that tests based on BLUS residuals have higher power than those based on the more popular recursive residuals, even in those cases (structural breaks) where intuition...