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Modeling the diffusion of scientific publications

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Author Info

  • Fok, Dennis
  • Franses, Philip Hans

Abstract

This paper illustrates that salient features of a panel of time series of annual citations can be captured by a Bass type diffusion model. We put forward an extended version of this diffusion model, where we consider the relation between key characteristics of the diffusion process and features of the articles. More specifically, parameters measuring citations' ceiling and the timing of peak citations are correlated with specific features of the articles like the number of pages and the number of authors. Our approach amounts to a multi-level non-linear regression for a panel of time series. We illustrate our model for citations to articles that were published in Econometrica and the Journal of Econometrics. Amongst other things, we find that more references lead to more citations and that for the Journal of Econometrics peak citations of more recent articles tend to occur later.

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File URL: http://www.sciencedirect.com/science/article/B6VC0-4MH2C6M-1/2/f68483bbc05242a667c4ef28972d02fc
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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 139 (2007)
Issue (Month): 2 (August)
Pages: 376-390

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Handle: RePEc:eee:econom:v:139:y:2007:i:2:p:376-390

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Web page: http://www.elsevier.com/locate/jeconom

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References

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  1. Hendrik P. van Dalen & Kene Henkens, 2000. "What makes a Scientific Article influential?," Tinbergen Institute Discussion Papers 00-032/1, Tinbergen Institute.
  2. Glenn Ellison, 2000. "The Slowdown of the Economics Publishing Process," NBER Working Papers 7804, National Bureau of Economic Research, Inc.
  3. Boswijk, H. Peter & Franses, Philip Hans, 2005. "On the Econometrics of the Bass Diffusion Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 255-268, July.
  4. Hendrik P. van Dalen & Kene Henkens, 2000. "What makes a Scientific Article influential?," Tinbergen Institute Discussion Papers 00-032/1, Tinbergen Institute.
  5. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
  6. Christophe Van den Bulte & Gary L. Lilien, 1997. "Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models," Marketing Science, INFORMS, vol. 16(4), pages 338-353.
  7. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
  8. Peter J. Lenk & Ambar G. Rao, 1990. "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures," Marketing Science, INFORMS, vol. 9(1), pages 42-53.
  9. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
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Citations

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Cited by:
  1. Boswijk, H. Peter & Franses, Philip Hans & van Dijk, Dick, 2010. "Cointegration in a historical perspective," Journal of Econometrics, Elsevier, vol. 158(1), pages 156-159, September.
  2. Franses, Ph.H.B.F., 2009. "Forecasting Sales," Econometric Institute Research Papers EI 2009-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Marc Fischer & Peter Leeflang & Peter Verhoef, 2010. "Drivers of peak sales for pharmaceutical brands," Quantitative Marketing and Economics, Springer, vol. 8(4), pages 429-460, December.
  4. Peter Howlett, 2008. "Travelling in the social science community: assessing the impact of the Indian Green Revolution across disciplines," Economic History Working Papers 22513, London School of Economics and Political Science, Department of Economic History.

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