We propose a class of distribution-free rank-based tests for the null hypothesis of a unit root. This class is indexed by the choice of a reference density g, which needs not coincide with the unknown actual innovation density f. The validity of these tests, in terms of exact finite sample size,...

We propose a class of distribution-free rank-based tests for the null hypothesis of a unit root. This class is indexed by the choice of a reference density g, which needs not coincide with the unknown actual innovation density f. The validity of these tests, in terms of exact finite sample size,...

This paper proposes a simple maximum likelihood regression estimator that outperforms Least Squares in terms of efficiency and mean square error for a large number of skewed and/or heavy tailed error distributions

This paper proposes a simple maximum likelihood regression estimator that outperforms Least Squares in terms of efficiency and mean square error for a large number of skewed and/or heavy tailed error distributions.

For multivariate Gaussian copula models with unknown margins and structured correlation matrices, a rank-based, semiparametrically effi cient estimator is proposed for the Euclidean copula parameter. This estimator is defined as a one-step update of a rank-based pilot estimator in the direction...