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Empirical modeling of the impact factor distribution

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  • Michał Brzeziński

    ()
    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The distribution of impact factors has been modeled in the recent informetric literature using two-exponent law proposed by Mansilla et al. (2007). This paper shows that two distributions widely-used in economics, namely the Dagum and Singh-Maddala models, possess several advantages over the two-exponent model. Compared to the latter, the former give as good as or slightly better fit to data on impact factors in eight important scientific fields. In contrast to the two-exponent model, both proposed distributions have closed-from probability density functions and cumulative distribution functions, which facilitates fitting these distributions to data and deriving their statistical properties.

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File URL: http://www.wne.uw.edu.pl/inf/wyd/WP/WNE_WP118.pdf
File Function: First version, 2014
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Bibliographic Info

Paper provided by Faculty of Economic Sciences, University of Warsaw in its series Working Papers with number 2014-01.

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Length: 16 pages
Date of creation: 2014
Date of revision:
Handle: RePEc:war:wpaper:2014-01

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Keywords: impact factor; two-exponent law; Dagum model; Singh-Maddala model; maximum likelihood estimation; model selection;

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  1. Stephen P. Jenkins, 2009. "Distributionally-Sensitive Inequality Indices And The Gb2 Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(2), pages 392-398, 06.
  2. Mishra, SK, 2010. "A note on empirical sample distribution of journal impact factors in major discipline groups," MPRA Paper 20747, University Library of Munich, Germany.
  3. Waltman, L. & van Eck, N.J.P., 2009. "Some Comments on Egghe’s Derivation of the Impact Factor Distribution," ERIM Report Series Research in Management ERS-2009-016-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  4. Mansilla, R. & Köppen, E. & Cocho, G. & Miramontes, P., 2007. "On the behavior of journal impact factor rank-order distribution," Journal of Informetrics, Elsevier, vol. 1(2), pages 155-160.
  5. Samuel Dastrup & Rachel Hartshorn & James McDonald, 2007. "The impact of taxes and transfer payments on the distribution of income: A parametric comparison," Journal of Economic Inequality, Springer, vol. 5(3), pages 353-369, December.
  6. Singh, S K & Maddala, G S, 1976. "A Function for Size Distribution of Incomes," Econometrica, Econometric Society, vol. 44(5), pages 963-70, September.
  7. Sarabia, José María & Prieto, Faustino & Trueba, Carmen, 2012. "Modeling the probabilistic distribution of the impact factor," Journal of Informetrics, Elsevier, vol. 6(1), pages 66-79.
  8. Kleiber, Christian, 1996. "Dagum vs. Singh-Maddala income distributions," Economics Letters, Elsevier, vol. 53(3), pages 265-268, December.
  9. Wilfling, Bernd & Kramer, Walter, 1993. "The Lorenz-ordering of Singh-Maddala income distributions," Economics Letters, Elsevier, vol. 43(1), pages 53-57.
  10. Christian Kleiber, 2007. "A Guide to the Dagum Distributions," Working papers 2007/23, Faculty of Business and Economics - University of Basel.
  11. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
  12. McDonald, James B, 1984. "Some Generalized Functions for the Size Distribution of Income," Econometrica, Econometric Society, vol. 52(3), pages 647-63, May.
  13. Egghe, L., 2009. "Mathematical derivation of the impact factor distribution," Journal of Informetrics, Elsevier, vol. 3(4), pages 290-295.
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