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Type: Journal Articles Authors: Walter Beckert and Daniel McFadden
Published in: Econometric Theory
Volume, issue, pages: Vol. 26, No. 2, pp. 469-500
Previous version: cemmap Working Papers [Details]
This paper considers parametric estimation problems with i.i.d. data. It focusses on rate-effciency, in the sense of maximal possible convergence rates of stochastically bounded 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 determine maximal possible convergence rates.
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