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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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    File URL: https://www.ifs.org.uk/uploads/CWP281818.pdf
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    References listed on IDEAS

    as
    1. An, Yonghong & Hu, Yingyao, 2012. "Well-posedness of measurement error models for self-reported data," Journal of Econometrics, Elsevier, vol. 168(2), pages 259-269.
    2. Anders Bohlmark & Matthew J. Lindquist, 2006. "Life-Cycle Variations in the Association between Current and Lifetime Income: Replication and Extension for Sweden," Journal of Labor Economics, University of Chicago Press, vol. 24(4), pages 879-900, October.
    3. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    4. repec:bla:scandj:v:119:y:2017:i:1:p:72-101 is not listed on IDEAS
    5. repec:aea:aejapp:v:10:y:2018:i:4:p:408-38 is not listed on IDEAS
    6. Marcus Hagedorn & Tzuo Hann Law & Iourii Manovskii, 2017. "Identifying Equilibrium Models of Labor Market Sorting," Econometrica, Econometric Society, vol. 85, pages 29-65, January.
    7. Valentino Dardanoni & Mario Fiorini & Antonio Forcina, 2012. "Stochastic monotonicity in intergenerational mobility tables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(1), pages 85-107, January.
    8. Yonghong An & Wang Le & Ruli Xiao, 2015. "Your American Dream is Not Mine! A New Approach to Estimating Intergenerational Mobility Elasticities," CAEPR Working Papers 2015-016, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    9. repec:bla:obuest:v:79:y:2017:i:1:p:79-100 is not listed on IDEAS
    10. Cristian Bartolucci & Francesco Devicienti & Ignacio Monzón, 2018. "Identifying Sorting in Practice," American Economic Journal: Applied Economics, American Economic Association, vol. 10(4), pages 408-438, October.
    11. Chen, Wen-Hao & Piraino, Patrizio & Ostrovsky, Yuri, 2016. "Intergenerational Income Transmission: New Evidence from Canada," Analytical Studies Branch Research Paper Series 2016379e, Statistics Canada, Analytical Studies Branch.
    12. Chen, Wen-Hao & Ostrovsky, Yuri & Piraino, Patrizio, 2017. "Lifecycle variation, errors-in-variables bias and nonlinearities in intergenerational income transmission: new evidence from Canada," Labour Economics, Elsevier, vol. 44(C), pages 1-12.
    13. Debopam Bhattacharya & Bhashkar Mazumder, 2011. "A nonparametric analysis of black–white differences in intergenerational income mobility in the United States," Quantitative Economics, Econometric Society, vol. 2(3), pages 335-379, November.
    14. Espen Bratberg & Jonathan Davis & Bhashkar Mazumder & Martin Nybom & Daniel D. Schnitzlein & Kjell Vaage, 2017. "A Comparison of Intergenerational Mobility Curves in Germany, Norway, Sweden, and the US," Scandinavian Journal of Economics, Wiley Blackwell, vol. 119(1), pages 72-101, January.
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    More about this item

    Keywords

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

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