Maximal uniform convergence rates in parametric estimation problems

Walter Beckert () and Daniel McFadden

No CWP06/05, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Keywords: parametric estimators; uniform convergence; Hellinger distance; Locally Asymptotically Quadratic (LAQ) Families (search for similar items in EconPapers)
JEL-codes: C13 C16 (search for similar items in EconPapers)
Pages: 20 pp.
Date: 2005-06-17
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http://cemmap.ifs.org.uk/wps/cwp0605.pdf (application/pdf)

Related works:
Journal Article: MAXIMAL UNIFORM CONVERGENCE RATES IN PARAMETRIC ESTIMATION PROBLEMS (2010)
Working Paper: Maximal uniform convergence rates in parametric estimation problems (2007)
Working Paper: Maximal Uniform Convergence Rates in Parametric Estimation Problems (2004)
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