  Estimating the innovation probability density is an important issue in any
regression analysis. This paper focuses on functional autoregressive models. A
residual-based kernel estimator is proposed for the innovation density.
Asymptotic properties of this estimator depend on the average prediction error
of the functional autoregressive function. Sufficient conditions are studied to
provide strong uniform consistency and asymptotic normality of the kernel
density estimator.
