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Flexible Parametric Models for Long-Tailed Patent Count Distributions

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  • Guo, Jie Q
  • Trivedi, Pravin K

Abstract

This article explores alternative approaches to modeling the relationship between the number of patents and research and development expenditure. Patent counts typically exhibit long upper tails that are inadequately modeled by standard Poisson and negative binomial regression models. We compare the performance of two relatively new "semiparametric" approaches with two flexible parametric approaches in analysing two patent data sets. Copyright 2002 by Blackwell Publishing Ltd

Suggested Citation

  • Guo, Jie Q & Trivedi, Pravin K, 2002. " Flexible Parametric Models for Long-Tailed Patent Count Distributions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(1), pages 63-82, February.
  • Handle: RePEc:bla:obuest:v:64:y:2002:i:1:p:63-82
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    Cited by:

    1. Shiferaw Gurmu & Fidel Pérez-Sebastián, 2008. "Patents, R&D and lag effects: evidence from flexible methods for count panel data on manufacturing firms," Empirical Economics, Springer, vol. 35(3), pages 507-526, November.
    2. Michele Cincera, 2005. "The link between firms' R&D by type of activity and source of funding and the decision to patent," DULBEA Working Papers 05-10.RS, ULB -- Universite Libre de Bruxelles.
    3. Alka Chadha, 2005. "Trips and Patenting Activity: Evidence from the Indian Pharmaceutical Industry," Departmental Working Papers wp0512, National University of Singapore, Department of Economics.
    4. Katsuhito Nohara & Masaki Narukawa, 2015. "Measuring lost recreational benefits in Fukushima due to harmful rumors using a Poisson-inverse Gaussian regression?," ERSA conference papers ersa15p344, European Regional Science Association.
    5. Hagedoorn, John & Wang, Ning, 2010. "Is there complementarity or substitutability between internal and external R&D strategies?," MERIT Working Papers 005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    6. Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
    7. Katsuhito Nohara, 2014. "Economic Valuation of the Damage to Tourism Benefits by Eastern Japan Great Earthquake Disaster," ERSA conference papers ersa14p1017, European Regional Science Association.
    8. Michael Creel & Montserrat Farell, 2005. "Modeling Usage of Medical Care Services: The Medical Expenditure Panel Survey Data, 1996-2000," UFAE and IAE Working Papers 646.05, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    9. Wang, Ning & Hagedoorn, John, 2014. "The lag structure of the relationship between patenting and internal R&D revisited," Research Policy, Elsevier, vol. 43(8), pages 1275-1285.
    10. Eskander Alvi & Debasri Mukherjee & Ashraf Eid, 2007. "Do Patent Protection and Technology Transfer Facilitate R&D in Developed and Emerging Countries? A Semiparametric Study," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 35(2), pages 217-231, June.
    11. Hagedoorn, John & Wang, Ning, 2012. "Is there complementarity or substitutability between internal and external R&D strategies?," Research Policy, Elsevier, vol. 41(6), pages 1072-1083.
    12. Robert Kaplon, 2010. "Heterogeneity in models of purchase frequency. A comparison of Poisson-gamma mixtures with finite Poisson mixtures," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 3, pages 41-52.

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