Weighted residual-based density estimators for nonlinear autoregressive models.
Abstract
We consider residual-based and randomly weighted kernel estimators for innovation densities of nonlinear autoregressive models.The weights are chosen to make use of the information that the innovations have mean zero. Rates of convergence are obtained in weighted L1-norms. These estimators give rise to smoothed and weighted empirical distribution functions and moments. It is shown that the latter are efficient if an estimator for the autoregression
parameter is used to construct the residuals.
