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On the efficiency of estimators in truncated height samples

Author

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  • Jacobs, Jan
  • Katzur, Tomek
  • Tassenaar, Vincent

    (Groningen University)

Abstract

We test the efficiency of estimators proposed for truncated height samples with a new data set of over 23,000 height observations covering nearly all conscripts in Drenthe, a province of the Netherlands, over the period 1826-1860. We find that the `best' estimator, truncated ML, in its unrestricted form overestimates the mean and underestimates the variance. If the variance is set to the population variance, the mean is underestimated. We question the normality assumption that is typically made in this literature. Our `population' is skewed, which might explain the poor performance of the estimators

Suggested Citation

  • Jacobs, Jan & Katzur, Tomek & Tassenaar, Vincent, 2004. "On the efficiency of estimators in truncated height samples," CCSO Working Papers 200408, University of Groningen, CCSO Centre for Economic Research.
    • Handle: RePEc:gro:rugccs:200408
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    File URL: http://irs.ub.rug.nl/ppn/268627126
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    References listed on IDEAS

    as
    1. A'Hearn, Brian & Komlos, John, 2003. "Improvements in Maximum Likelihood Estimators of Truncated Normal Samples with Prior Knowledge of σ," Discussion Papers in Economics 51, University of Munich, Department of Economics.
    2. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    3. Komlos, John, 2003. "How to (and How Not to) Analyze Deficient Height Samples," Discussion Papers in Economics 56, University of Munich, Department of Economics.
    4. Richard H. Steckel, 1995. "Stature and the Standard of Living," Journal of Economic Literature, American Economic Association, vol. 33(4), pages 1903-1940, December.
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    Cited by:

    1. Yoko Akachi & David Canning, 2007. "The Height of Women in Sub-Saharan Africa: the Role of Health, Nutrition, and Income in Childhood," PGDA Working Papers 2207, Program on the Global Demography of Aging.

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