Abstract. This paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust...

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semipara- metric general trimmed estimator (GTE) of...

A new class of robust regression estimators is proposed that forms an alternative to traditional robust one-step estimators and that achieves the √n rate of convergence irrespective of the initial estimator under a wide range of distributional assumptions. The proposed reweighted least trimmed...

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...

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....

The three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian and Prucha (2007), which corrects for spatially correlated errors in static panel data models, is extended by introducing fixed effects, a spatial lag, and a one-period lag of the dependent variable as additional...

We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining...

We analyse gender wage inequalities in Italy in the mid-1990s and in the mid-2000s. In this period important labour market developments occurred: institutional changes have loosened the use of flexible and atypical contracts; the female employment rates and educational levels have substantially...

Abstract: This paper studies the gender wage gap by educational attainment in Italy using the 1994–2001 ECHP data. We estimate wage distributions in the presence of covariates and sample selection separately for highly and low educated men and women. Then, we decompose the gender wage gap...

A major attraction of panel data is the ability to estimate dynamic models on an individual level. Moffitt (1993) and Collado (1998) have argued that such models can also be identified from repeated cross-section data. In this paper we reconsider this issue. We review the identification...