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Measurement error and rank correlations

Author

Listed:
  • Toru Kitagawa

    () (Institute for Fiscal Studies and cemmap and University College London)

  • Martin Nybom

    (Institute for Fiscal Studies)

  • Jan Stuhler

    (Institute for Fiscal Studies)

Abstract

This paper characterizes and proposes a method to correct for errors-in-variables biases in the estimation of rank correlation coeffcients (Spearman's ? and Kendall's t). We first investigate a set of suffcient conditions under which measurement errors bias the sample rank correlations toward zero. We then provide a feasible nonparametric bias-corrected estimator based on the technique of small error variance approximation. We assess its performance in simulations and an empirical application, using rich Swedish data to estimate intergenerational rank correlations in income. The method performs well in both cases, lowering the mean squared error by 50-85 percent already in moderately sized samples (n = 1,000).

Suggested Citation

  • Toru Kitagawa & Martin Nybom & Jan Stuhler, 2018. "Measurement error and rank correlations," CeMMAP working papers CWP28/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:28/18
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    References listed on IDEAS

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    Cited by:

    1. Brantly Callaway & Weige Huang, 2020. "Distributional Effects of a Continuous Treatment with an Application on Intergenerational Mobility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 808-842, August.

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    Keywords

    Errors-in-variables; Spearman's rank correlation; Kendall's tau; Small variance approximation; Intergenerational mobility.;
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