  In multivariate regression estimation, the rate of convergence depends on the
dimension of the regressor. This fact, known as the curse of the
dimensionality, motivated several works. The additive model, introduced by
Stone (10), offers an efficient response to this problem. In the setting of
continuous time processes, using the marginal integration method, we obtain the
quadratic convergence rate and the asymptotic normality of the components of
the additive model.
