Maximal Uniform Convergence Rates in Parametric Estimation Problems
AbstractThis paper considers parametric estimation problems with i.i.d. data. It focusses on rate-efficiency, 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 InfoPaper provided by Birkbeck, Department of Economics, Mathematics & Statistics in its series Birkbeck Working Papers in Economics and Finance with number 0405.
Date of creation: Nov 2004
Date of revision:
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Other versions of this item:
- Beckert, Walter & McFadden, Daniel L., 2010. "Maximal Uniform Convergence Rates In Parametric Estimation Problems," Econometric Theory, Cambridge University Press, vol. 26(02), pages 469-500, April.
- 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, 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
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