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Maximal Uniform Convergence Rates in Parametric Estimation Problems

Author

Listed:
  • Walter Beckert

    (Department of Economics, Mathematics & Statistics, Birkbeck)

  • Daniel McFadden

Abstract

This 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.

Suggested Citation

  • 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.
    • Handle: RePEc:bbk:bbkefp:0405
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    File URL: https://eprints.bbk.ac.uk/id/eprint/27107
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    2. McFadden, Daniel, 2022. "Instability in mixed logit demand models," Journal of choice modelling, Elsevier, vol. 43(C).

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    JEL classification:

    • 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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