IDEAS home Printed from https://ideas.repec.org/p/hhs/cesisp/0257.html
   My bibliography  Save this paper

A Ridge Regression estimator for the zero-inflated Poisson model

Author

Listed:
  • Kibria, B. M. Golam

    (Florida International University)

  • Månsson, Kristofer

    (Jönköping University)

  • Shukur, Ghazi

    (Linnaeus University)

Abstract

The zero inflated Poisson regression model is very common when analysing economic data that comes in the form of non-negative integers since it accounts for excess zeros and over-dispersion of the dependent variable. This model may be used in innovation analysis to see for example the impact on different economic factors has on the number of patents of companies, how frequently mortgages or credit-card fail to meet their financial obligations, the amount of take-over bids firms receives and the number of products different firms export, etc. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression estimators and some methods of estimating the ridge parameter k for the non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both MSE and MAE are considered as performance criterion. The simulation study shows that some estimators are better than the commonly applied maximum likelihood estimator and some other ridge regression estimators. Some useful estimators are recommended for the practitioners.

Suggested Citation

  • Kibria, B. M. Golam & Månsson, Kristofer & Shukur, Ghazi, 2011. "A Ridge Regression estimator for the zero-inflated Poisson model," Working Paper Series in Economics and Institutions of Innovation 257, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    • Handle: RePEc:hhs:cesisp:0257
    as

    Download full text from publisher

    File URL: https://static.sys.kth.se/itm/wp/cesis/cesiswp257.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. William H. Greene, 1994. "Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models," Working Papers 94-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    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. M. Revan Özkale & Atif Abbasi, 2022. "Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm," Statistical Papers, Springer, vol. 63(6), pages 1979-2040, December.
    2. Muhammad Amin & Muhammad Qasim & Muhammad Amanullah & Saima Afzal, 2020. "Performance of some ridge estimators for the gamma regression model," Statistical Papers, Springer, vol. 61(3), pages 997-1026, June.
    3. Akhil Rao & Francesca Letizia, 2022. "An integrated debris environment assessment model," Papers 2205.05205, arXiv.org.

    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. Cornelia Lawson, 2013. "Academic Inventions Outside the University: Investigating Patent Ownership in the UK," Industry and Innovation, Taylor & Francis Journals, vol. 20(5), pages 385-398, July.
    2. Rui Baptista & Joana Mendonça, 2010. "Proximity to knowledge sources and the location of knowledge-based start-ups," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(1), pages 5-29, August.
    3. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    4. Christopher J. W. Zorn, 1998. "An Analytic and Empirical Examination of Zero-Inflated and Hurdle Poisson Specifications," Sociological Methods & Research, , vol. 26(3), pages 368-400, February.
    5. Agrawal, Ajay & Cockburn, Iain, 2003. "The anchor tenant hypothesis: exploring the role of large, local, R&D-intensive firms in regional innovation systems," International Journal of Industrial Organization, Elsevier, vol. 21(9), pages 1227-1253, November.
    6. Timothy C. Haab, "undated". "A Utility Based Repeated Discrete Choice Model of Consumer Demand," Working Papers 9611, East Carolina University, Department of Economics.
    7. Niklas Elert, 2014. "What determines entry? Evidence from Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 55-92, August.
    8. Boubaker, Sabri & Labégorre, Florence, 2008. "Ownership structure, corporate governance and analyst following: A study of French listed firms," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 961-976, June.
    9. Simen G. Enger & Fulvio Castellacci, 2016. "Who gets Horizon 2020 research grants? Propensity to apply and probability to succeed in a two-step analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1611-1638, December.
    10. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    11. Abbas Moghimbeigi & Mohammed Reza Eshraghian & Kazem Mohammad & Brian Mcardle, 2008. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1193-1202.
    12. Nicolas Carayol, 2006. "La production de brevets par les chercheurs et enseignants-chercheurs.. Le cas de l'université Louis Pasteur," Economie & Prévision, La Documentation Française, vol. 0(4), pages 117-134.
    13. Levan Elbakidze & Rodolfo M. Nayga Jr. & Hao Li & Chris McIntosh, 2014. "Value elicitation for multiple quantities of a quasi-public good using open ended choice experiments and uniform price auctions," Agricultural Economics, International Association of Agricultural Economists, vol. 45(2), pages 253-265, March.
    14. Jinyoung Kim & Kanghyock Koh, 2014. "Incentives for Journal Editors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(1), pages 348-371, February.
    15. Philippe Aghion & Richard Blundell & Rachel Griffith & Peter Howitt & Susanne Prantl, 2009. "The Effects of Entry on Incumbent Innovation and Productivity," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 20-32, February.
    16. List, John A., 2001. "US county-level determinants of inbound FDI: evidence from a two-step modified count data model," International Journal of Industrial Organization, Elsevier, vol. 19(6), pages 953-973, May.
    17. J. M. C. Santos Silva & Silvana Tenreyro, 2015. "Trading Partners and Trading Volumes: Implementing the Helpman–Melitz–Rubinstein Model Empirically," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 93-105, February.
    18. Mahmut Yaşar & Catherine Paul, 2012. "Firm performance and knowledge spillovers from academic, industrial and foreign linkages: the case of China," Journal of Productivity Analysis, Springer, vol. 38(3), pages 237-253, December.
    19. Duch-Brown, Néstor & Rossetti, Fiammetta, 2020. "Digital platforms across the European regional energy markets," Energy Policy, Elsevier, vol. 144(C).
    20. Cameron, Trudy Ann & Englin, Jeffrey, 1997. "Respondent Experience and Contingent Valuation of Environmental Goods," Journal of Environmental Economics and Management, Elsevier, vol. 33(3), pages 296-313, July.

    More about this item

    Keywords

    Count data; Innovation analysis; Multicollinearity; Zero Inflated Poisson; Ridge Regression;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

    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:hhs:cesisp:0257. 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: Vardan Hovsepyan (email available below). General contact details of provider: https://edirc.repec.org/data/cekthse.html .

    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.