This paper considers parametric estimation problems with i.i.d. data. It focusseson rate-effciency, in the sense of maximal possible convergence rates of stochasticallybounded estimators, as an optimality criterion, largely unexplored in parametric estimation. Under mild conditions, the Hellinger metric, defined on the space of parametric probability measures, is shown to be an essentially universally applicable tool to determinemaximal possible convergence rates.
Centre for microdata methods and practice