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Composing the cumulative quantile regression function and the Goldie concentration curve

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  • Tse, SzeMan

Abstract

The model we discuss in this paper deals with inequality in distribution in the presence of a covariate. To elucidate that dependence, we propose to consider the composition of the cumulative quantile regression (CQR) function and the Goldie concentration curve, the standardized counterpart of which gives a fraction to fraction plot of the response and the covariate. It has the merit of enhancing the visibility of inequality in distribution when the latter is present. We shall examine the asymptotic properties of the corresponding empirical estimator. The associated empirical process involves a randomly stopped partial sum process of induced order statistics. Strong Gaussian approximations of the processes are constructed. The result forms the basis for the asymptotic theory of functional statistics based on these processes.

Suggested Citation

  • Tse, SzeMan, 2011. "Composing the cumulative quantile regression function and the Goldie concentration curve," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 674-682, March.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:3:p:674-682
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    6. Schechtman, Edna & Shelef, Amit & Yitzhaki, Shlomo & Zitikis, Ričardas, 2008. "Testing Hypotheses About Absolute Concentration Curves And Marginal Conditional Stochastic Dominance," Econometric Theory, Cambridge University Press, vol. 24(4), pages 1044-1062, August.
    7. Furman, Edward & Zitikis, Ricardas, 2008. "Weighted risk capital allocations," Insurance: Mathematics and Economics, Elsevier, vol. 43(2), pages 263-269, October.
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    Cited by:

    1. Bosco, Bruno, 2019. "One size does not fit all: Quantile regression estimates of cross-country risk of poverty in Europe," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 280-299.

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