In this paper we show that for D-optimal design, departures from the design are much less important than a depar-ture from a model. As a consequence, we propose, based on D-optimality, a rule for choosing the regression degree. We also study different types of departures from the model to define...

This paper is concerned with composite quantile regression for single-index models. Under mild conditions, we show that the linear composite quantile regression offers a consistent estimate of the index parameter vector. With a root-n consistent estimate of the index vector, the unknown link...

Motivated by the first-differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogenous regressors. The estimators utilize the additive structure of the first-differenced...