  There has been substantial recent work on methods for estimating the slope
function in linear regression for functional data analysis. However, as in the
case of more conventional finite-dimensional regression, much of the practical
interest in the slope centers on its application for the purpose of prediction,
rather than on its significance in its own right. We show that the problems of
slope-function estimation, and of prediction from an estimator of the slope
function, have very different characteristics. While the former is
intrinsically nonparametric, the latter can be either nonparametric or
semiparametric. In particular, the optimal mean-square convergence rate of
predictors is $n^{-1}$, where $n$ denotes sample size, if the predictand is a
sufficiently smooth function. In other cases, convergence occurs at a
polynomial rate that is strictly slower than $n^{-1}$. At the boundary between
these two regimes, the mean-square convergence rate is less than $n^{-1}$ by
only a logarithmic factor. More generally, the rate of convergence of the
predicted value of the mean response in the regression model, given a
particular value of the explanatory variable, is determined by a subtle
interaction among the smoothness of the predictand, of the slope function in
the model, and of the autocovariance function for the distribution of
explanatory variables.
