Maximal Uniform Convergence Rates In Parametric Estimation Problems
AbstractThis 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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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 26 (2010)
Issue (Month): 02 (April)
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Other versions of this item:
- Walter Beckert & Daniel McFadden, 2005. "Maximal uniform convergence rates in parametric estimation problems," CeMMAP working papers CWP06/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Walter Beckert & Daniel McFadden, 2004. "Maximal Uniform Convergence Rates in Parametric Estimation Problems," Birkbeck Working Papers in Economics and Finance 0405, Birkbeck, Department of Economics, Mathematics & Statistics.
- Walter Beckert & Daniel McFadden, 2007. "Maximal uniform convergence rates in parametric estimation problems," CeMMAP working papers CWP28/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- repec:cup:etheor:v:9:y:1993:i:1:p:1-18 is not listed on IDEAS
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