IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v171y2018icp245-248.html
   My bibliography  Save this article

Accounting for persistence in panel count data models. An application to the number of patents awarded

Author

Listed:
  • Dimitrakopoulos, Stefanos

Abstract

We propose a Poisson regression model that controls for three potential sources of persistence in panel count data; dynamics, latent heterogeneity and serial correlation in the idiosyncratic errors. We also account for the initial conditions problem. For model estimation, we develop a Markov Chain Monte Carlo algorithm. The proposed methodology is illustrated by a real example on the number of patents granted.

Suggested Citation

  • Dimitrakopoulos, Stefanos, 2018. "Accounting for persistence in panel count data models. An application to the number of patents awarded," Economics Letters, Elsevier, vol. 171(C), pages 245-248.
    • Handle: RePEc:eee:ecolet:v:171:y:2018:i:c:p:245-248
    DOI: 10.1016/j.econlet.2018.08.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176518303112
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2018.08.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blundell, Richard & Griffith, Rachel & Van Reenen, John, 1995. "Dynamic Count Data Models of Technological Innovation," Economic Journal, Royal Economic Society, vol. 105(429), pages 333-344, March.
    2. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, September.
    3. Hall, Bronwyn H & Griliches, Zvi & Hausman, Jerry A, 1986. "Patents and R and D: Is There a Lag?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 27(2), pages 265-283, June.
    4. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    5. Martin Biewen, 2009. "Measuring state dependence in individual poverty histories when there is feedback to employment status and household composition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1095-1116, November.
    6. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    7. Chib, Siddhartha & Jeliazkov, Ivan, 2006. "Inference in Semiparametric Dynamic Models for Binary Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 685-700, June.
    8. Montalvo, Jose G, 1997. "GMM Estimation of Count-Panel-Data Models with Fixed Effects and Predetermined Instruments," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 82-89, January.
    9. 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.
    10. Chib, Siddhartha & Greenberg, Edward & Winkelmann, Rainer, 1998. "Posterior simulation and Bayes factors in panel count data models," Journal of Econometrics, Elsevier, vol. 86(1), pages 33-54, June.
    11. Crepon, Bruno & Duguet, Emmanuel, 1997. "Estimating the Innovation Function from Patent Numbers: GMM on Count Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 243-263, May-June.
    12. Cincera, Michele, 1997. "Patents, R&D, and Technological Spillovers at the Firm Level: Some Evidence from Econometric Count Models for Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 265-280, May-June.
    13. Richard Blundell & Rachel Griffith & John van Reenen, 1999. "Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(3), pages 529-554.
    14. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    15. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, June.
    16. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters, in: Studies in Labor Markets, pages 91-140, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Siele Jean Tuo & Chang Li & Ettien Fulgence Brou & Diby Francois Kassi & Yobouet Thierry Gnangoin, 2024. "Estimating the carbon dioxide emission levels of G7 countries: A count data approach," Energy & Environment, , vol. 35(4), pages 1753-1772, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yoshitsugu Kitazawa, 2012. "An improved theoretical ground for the linear feedback model and a new indicator," Discussion Papers 58, Kyushu Sangyo University, Faculty of Economics.
    2. Ann-Kathrine Ejsing & Ulrich Kaiser & Hans Christian Kongsted & Keld Laursen, 2013. "The Role of University Scientist Mobility for Industrial Innovation," Working Papers 332, University of Zurich, Department of Business Administration (IBW).
    3. 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.
    4. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    5. Kaiser, Ulrich & Kongsted, Hans Christian & Rønde, Thomas, 2015. "Does the mobility of R&D labor increase innovation?," Journal of Economic Behavior & Organization, Elsevier, vol. 110(C), pages 91-105.
    6. Yoshitsugu Kitazawa, 2007. "Some additional moment conditions for a dynamic count panel data model," Discussion Papers 29, Kyushu Sangyo University, Faculty of Economics, revised Aug 2008.
    7. Uchida, Yuichiro & Cook, Paul, 2005. "Innovation and Market Structure in the Manufacturing Sector: An Application of Linear Feedback Models," Centre on Regulation and Competition (CRC) Working papers 30702, University of Manchester, Institute for Development Policy and Management (IDPM).
    8. Hagedoorn, John & Wang, Ning, 2010. "Is there complementarity or substitutability between internal and external R&D strategies?," MERIT Working Papers 2010-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    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. AndreÌ s LoÌ pez & Eugenia Orlicki, 2009. "Who Uses the Patent System in Developing Countries? A Study of Patent Propensities in Argentina, 1992-2001," Microeconomics Working Papers 22785, East Asian Bureau of Economic Research.
    11. Sunil Kanwar & Shailu Singh, 2016. "The Innovation-R&D Nexus- Evidence from the Indian Manufacturing Sector," Working papers 265, Centre for Development Economics, Delhi School of Economics.
    12. Czarnitzki, Dirk & Kraft, Kornelius & Thorwarth, Susanne, 2009. "The knowledge production of 'R' and 'D'," Economics Letters, Elsevier, vol. 105(1), pages 141-143, October.
    13. 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.
    14. Sunil Kanwar & Shailu Singh, 2018. "The Innovation†R&D Nexus in an Emerging Economy: Evidence from the Indian Manufacturing Sector," Australian Economic Papers, Wiley Blackwell, vol. 57(1), pages 35-54, March.
    15. Blazsek, Szabolcs & Escribano, Alvaro, 2010. "Knowledge spillovers in US patents: A dynamic patent intensity model with secret common innovation factors," Journal of Econometrics, Elsevier, vol. 159(1), pages 14-32, November.
    16. Chadha, Alka, 2009. "TRIPs and patenting activity: Evidence from the Indian pharmaceutical industry," Economic Modelling, Elsevier, vol. 26(2), pages 499-505, March.
    17. Lazkano, Itziar & Nøstbakken, Linda & Pelli, Martino, 2017. "From fossil fuels to renewables: The role of electricity storage," European Economic Review, Elsevier, vol. 99(C), pages 113-129.
    18. Emilie-Pauline Gallié & Diègo Legros, 2012. "Firms’ human capital, R&D and innovation: a study on French firms," Empirical Economics, Springer, vol. 43(2), pages 581-596, October.
    19. Beneito, Pilar & Rochina-Barrachina, María Engracia & Sanchis, Amparo, 2015. "The path of R&D efficiency over time," International Journal of Industrial Organization, Elsevier, vol. 42(C), pages 57-69.
    20. Kornelius Kraft & Jörg Stank & Ralf Dewenter, 2011. "Co-determination and innovation," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 35(1), pages 145-172.

    More about this item

    Keywords

    Dynamics; Initial conditions; Latent heterogeneity; Markov Chain Monte Carlo; Panel count data; Serial correlation;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:171:y:2018:i:c:p:245-248. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.