Some convenient limit properties of usual information criteria are given for cointegrating rank selection. Allowing for a non-parametric short memory component and using a reduced rank regression with only a single lag, standard information criteria are shown to be weakly consistent in the choice of cointegrating rank provided the penalty coefficient Cn → ∞ and Cn/n → 0 as n → ∞. The limit dist...

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank. Also instead of the projection matrix only the regression matrix was calculated. These problems are ...

Quantile regression for right- or left-censored outcomes has attracted attention due to its ability accommodate heterogeneity in analysis of survival times. Rank-based inferential methods have desirable properties quantile analysis, but censored data poses challenges the general concept ranking. In this article, we propose a notion rank scores, which enables us construct rank-based tests coeffi...

Rank-based estimates are asymptotically efficient when optimal scores are used. This paper describes a method for estimating the optimal score function based on residuals from an initial fit. The resulting adaptive estimate is shown to be asymptotically efficient.