IDEAS home Printed from https://ideas.repec.org/a/wly/jjmath/v2025y2025i1n3104487.html

Improved Efficiency in Generalized Poisson Hurdle Model Estimation Using Restricted and Shrinkage Methods

Author

Listed:
  • Hayder Hasan Rahmah Al-Gharrawi
  • Hossein Bevrani
  • Basim Shlaibah Msallam

Abstract

This paper investigates the use of shrinkage estimators in the generalized Poisson hurdle (GPH) model for count data analysis. The GPH model effectively handles data with both excess zeros and over‐ or underdispersion. We propose shrinkage estimators to improve parameter estimation in this model and analyze their asymptotic properties, including biases and risks. An extensive comparison through Monte Carlo simulations evaluates the efficacy of the suggested estimators against the maximum likelihood estimator, employing a simulated relative efficiency criterion. In addition, we apply the estimators to two real‐world datasets. Our findings illustrate that the suggested shrinkage estimators yield superior results compared to the traditional maximum likelihood estimator.

Suggested Citation

  • Hayder Hasan Rahmah Al-Gharrawi & Hossein Bevrani & Basim Shlaibah Msallam, 2025. "Improved Efficiency in Generalized Poisson Hurdle Model Estimation Using Restricted and Shrinkage Methods," Journal of Mathematics, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jjmath:v:2025:y:2025:i:1:n:3104487
    DOI: 10.1155/jom/3104487
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/jom/3104487
    Download Restriction: no
    File URL: https://libkey.io/10.1155/jom/3104487?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
    ---><---

    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, Enero-Abr.
    2. Seyed Ehsan Saffari & Robiah Adnan & William Greene, 2013. "Investigating the impact of excess zeros on hurdle-generalized Poisson regression model with right censored count data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(1), pages 67-80, February.
    3. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    4. 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.
    5. Wang, Peiming & Alba, Joseph D., 2006. "A zero-inflated negative binomial regression model with hidden Markov chain," Economics Letters, Elsevier, vol. 92(2), pages 209-213, August.
    Full references (including those not matched with items on IDEAS)

    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. Agostino Capponi & Zhaonan Qu, 2025. "Handling Sparse Non-negative Data in Finance," Papers 2509.01478, arXiv.org.
    2. Jiang, Yuan & House, Lisa A., "undated". "Comparison of the Performance of Count Data Models under Different Zero-Inflation Scenarios Using Simulation Studies," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258342, Agricultural and Applied Economics Association.
    3. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    4. 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.
    5. 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.
    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. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    9. 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.
    10. J. M. C. Santos Silva & Silvana Tenreyro, 2022. "The Log of Gravity at 15," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 21(3), pages 423-437, September.
    11. Deborah Kanda & Jingjing Yin & Xinyan Zhang & Hani Samawi, 2025. "Efficient regression analyses with zero-augmented models based on ranking," Computational Statistics, Springer, vol. 40(2), pages 601-632, February.
    12. Chiara Bocci & Laura Grassini & Emilia Rocco, 2021. "A multiple inflated negative binomial hurdle regression model: analysis of the Italians’ tourism behaviour during the Great Recession," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1109-1133, October.
    13. Buczkowska, Sabina & de Lapparent, Matthieu, 2014. "Location choices of newly created establishments: Spatial patterns at the aggregate level," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 68-81.
    14. Daniel Biftu Bekalo & Dufera Tejjeba Kebede, 2021. "Zero-Inflated Models for Count Data: An Application to Number of Antenatal Care Service Visits," Annals of Data Science, Springer, vol. 8(4), pages 683-708, December.
    15. Drivas, Kyriakos & Economidou, Claire & Karamanis, Dimitrios & Sanders, Mark, 2020. "Mobility of highly skilled individuals and local innovation activity," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    16. Jose‐Maria Garcia‐Alvarez‐Coque & Ibtissem Taghouti & Victor Martinez‐Gomez, 2020. "Changes in Aflatoxin Standards: Implications for EU Border Controls of Nut Imports," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(3), pages 524-541, September.
    17. José M. R. Murteira & Mário A. G. Augusto, 2017. "Hurdle models of repayment behaviour in personal loan contracts," Empirical Economics, Springer, vol. 53(2), pages 641-667, September.
    18. Gregory N. Price, 2019. "Does Productivity in the Formal Food Sector Drive Human Ebola Virus Infections in Sub‐Saharan Africa?," African Development Review, African Development Bank, vol. 31(2), pages 167-178, June.
    19. Hossein Kavand & Marcel Voia, 2018. "Estimation of Health Care Demand and its Implication on Income Effects of Individuals," Springer Proceedings in Business and Economics, in: William H. Greene & Lynda Khalaf & Paul Makdissi & Robin C. Sickles & Michael Veall & Marcel-Cristia (ed.), Productivity and Inequality, pages 275-304, Springer.
    20. Rainer Winkelmann, 2015. "Counting on count data models," World of Labour, LISER, pages 148-148, May.

    More about this item

    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:wly:jjmath:v:2025:y:2025:i:1:n:3104487. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/1469 .

    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.