Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79326
Authors: 
Beckert, Walter
McFadden, Daniel L.
Year of Publication: 
2005
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP06/05
Abstract: 
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.
Subjects: 
parametric estimators , uniform convergence , Hellinger distance , Locally Asymptotically Quadratic (LAQ) Families
JEL: 
C13
C16
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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