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Estimating the Number of Patents in the World Using Count Panel Data Models

Author

Listed:
  • Youssef, Ahmed H.
  • Abonazel, Mohamed R.
  • Ahmed, Elsayed G.

Abstract

In this paper, we review some estimators of count regression (Poisson and negative binomial) models in panel data modeling. These estimators based on the type of the panel data model (the model with fixed or random effects). Moreover, we study and compare the performance of these estimators based on a real dataset application. In our application, we study the effect of some economic variables on the number of patents for seventeen high-income countries in the world over the period from 2005 to 2016. The results indicate that the negative binomial model with fixed effects is the better and suitable for data, and the important (statistically significant) variables that effect on the number of patents in high-income countries are research and development (R&D) expenditures and gross domestic product (GDP) per capita.

Suggested Citation

  • Youssef, Ahmed H. & Abonazel, Mohamed R. & Ahmed, Elsayed G., 2020. "Estimating the Number of Patents in the World Using Count Panel Data Models," MPRA Paper 100749, University Library of Munich, Germany, revised 19 Mar 2020.
    • Handle: RePEc:pra:mprapa:100749
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    References listed on IDEAS

    as
    1. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, June.
    2. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    3. Angers, Jean-François & Desjardins, Denise & Dionne, Georges & Guertin, François, 2018. "Modelling And Estimating Individual And Firm Effects With Count Panel Data," ASTIN Bulletin, Cambridge University Press, vol. 48(3), pages 1049-1078, September.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    5. Baltagi, Badi H., 2015. "The Oxford Handbook of Panel Data," OUP Catalogue, Oxford University Press, number 9780199940042, Decembrie.
    6. Peiming Wang & Iain Cockburn & Martin L. Puterman, "undated". "A Mixed Poisson Regression Model for Analysis of Patent Data," Computing in Economics and Finance 1996 _049, Society for Computational Economics.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Rufei Ma & Xu He & Xin Xiang, 2022. "Cross‐listing on the Hong Kong Exchange and Chinese firm innovation: New evidence," Australian Economic Papers, Wiley Blackwell, vol. 61(2), pages 365-393, June.
    2. Francesca Pantaleone & Roberto Fazioli, 2022. "Lock-In Effects on the Energy Sector: Evidence from Hydrogen Patenting Activities," Energies, MDPI, vol. 15(9), pages 1-15, April.

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    More about this item

    Keywords

    Conditional maximum likelihood estimation; fixed effects model; Hausman test; negative binomial regression; Poisson regression; random effects model.;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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