This paper considers nonparametric identification and estimation of the regression function when a covariate is mismeasured. The measurement error need not be classical. Employing the small measurement error approximation, we establish nonparametric identification under weak and...

This paper proposes a powerful alternative to the t-test of the null hypothesis that a coefficient in linear regression is equal to zero when a regressor is mismeasured. We assume there are two contaminated measurements of the regressor of interest. We allow the two measurement errors to be...

The aim of this study is to analyse how research and innovation activities, funded by the Nanosciences and Nanotechnologies, Materials, New Production Technologies (NMP) and the Industrial Biotechnology (B) themes in the Seventh Framework Programme for Research (FP7) (together: FP7 NMBP), are...

This article shows how to construct a likelihood for a general class of censoring problems. This likelihood is proven to be valid, i.e. its maximiser is consistent and the respective root-n estimator is asymptotically efficient and normally distributed under regularity conditions. The method...

Rating variables indicate the extent to which a quality is present, or absent, in a unit of observation. In this paper, we discuss a class of non-linear regression models for rating dependent variables and their estimation by parametric and semiparametric methods. An application to life...

We test for mean reversion in real exchange rates using a recently developed unit root test for non-normal processes based on quantile autoregression inference in semi-parametric and non-parametric settings. The quantile regression approach allows us to directly capture the impact of different...

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...