Before revision:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment . We argue that in general a large value of n is better than a large value of T. Conditions for asymptotic normality of linear and nonlinear functionals of the estimated function of interest are derived.
After revision:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment and argue that a large nis often better than a large value of T. Conditions for asymptotic normality of linear and nonlinear functionals of the estimated function of interest are derived.