A new multivariate random walk model with slowly changing parameters is introduced and investigated in detail. Nonparametric estimation of local covariance matrix is proposed. The asymptotic distributions, including asymptotic biases, variances and covariances of the proposed estimators are...

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). With...

The aim of this work is to review the paper by Hellerstein & Imbens (1982) focusing on the use of auxiliary data and a formal derivation of the asymptotic properties of the underlying Weighted Least Squares estimator.