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Quantile Kernel Regression for Identifying Excellent Economists

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  • Richard S.J. Tol

    ()
    (Department of Economics, University of Sussex
    Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands
    Department of Spatial Economics, Vrije Universiteit, Amsterdam, The Netherlands)

Abstract

Quantile kernel regression is a flexible way to estimate the percentile of a scholar’s quality stratified by a measurable characteristic, without imposing inappropriate assumption about functional form or population distribution. Quantile kernel regression is here applied to identifying the one-in-a-hundred economist per age cohort according to the Hirsch number.

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Bibliographic Info

Paper provided by Department of Economics, University of Sussex in its series Working Paper Series with number 6013.

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Date of creation: Apr 2013
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Handle: RePEc:sus:susewp:6013

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Keywords: quantile kernel regression; Hirsch number; economics;

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  1. Herranz, Neus & Ruiz-Castillo, Javier, 2011. "Sub-field normalization in the multiplicative case: Average-based citation indicators," CEPR Discussion Papers, C.E.P.R. Discussion Papers 8715, C.E.P.R. Discussion Papers.
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  3. Tol, Richard S.J., 2013. "The Matthew effect for cohorts of economists," Journal of Informetrics, Elsevier, Elsevier, vol. 7(2), pages 522-527.
  4. Falk, Michael, 1986. "On the estimation of the quantile density function," Statistics & Probability Letters, Elsevier, Elsevier, vol. 4(2), pages 69-73, March.
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  6. Christian Zimmermann, 2012. "Academic rankings with RePEc," Working Papers, Federal Reserve Bank of St. Louis 2012-023, Federal Reserve Bank of St. Louis.
  7. Beirlant, Jan & Glänzel, Wolfgang & Carbonez, An & Leemans, Herlinde, 2007. "Scoring research output using statistical quantile plotting," Journal of Informetrics, Elsevier, Elsevier, vol. 1(3), pages 185-192.
  8. Richard S. J. Tol, 2013. "The Matthew Effect for Cohorts of Economists," Working Paper Series, Department of Economics, University of Sussex 5513, Department of Economics, University of Sussex.
  9. Sarabia, José María & Prieto, Faustino & Trueba, Carmen, 2012. "Modeling the probabilistic distribution of the impact factor," Journal of Informetrics, Elsevier, Elsevier, vol. 6(1), pages 66-79.
  10. van Raan, Anthony F.J. & van Leeuwen, Thed N. & Visser, Martijn S. & van Eck, Nees Jan & Waltman, Ludo, 2010. "Rivals for the crown: Reply to Opthof and Leydesdorff," Journal of Informetrics, Elsevier, Elsevier, vol. 4(3), pages 431-435.
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  1. > Economics Profession > Ranking in Economics > Ranking Methodology

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