IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v91y2024ics0038012123002732.html
   My bibliography  Save this article

Fuzzy multiple regressions for Cross-Section and Panel data

Author

Listed:
  • Belhadj, Besma

Abstract

A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and annual population growth rate on poverty in the Middle East and North Africa (MENA) region.

Suggested Citation

  • Belhadj, Besma, 2024. "Fuzzy multiple regressions for Cross-Section and Panel data," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:soceps:v:91:y:2024:i:c:s0038012123002732
    DOI: 10.1016/j.seps.2023.101761
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012123002732
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2023.101761?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. D'Urso, Pierpaolo, 2003. "Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 47-72, February.
    2. Besma Belhadj, 2011. "A new fuzzy unidimensional poverty index from an information theory perspective," Empirical Economics, Springer, vol. 40(3), pages 687-704, May.
    3. D'Urso, Pierpaolo & Gastaldi, Tommaso, 2000. "A least-squares approach to fuzzy linear regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 427-440, October.
    4. Besma Belhadj & Firas Kaabi, 2020. "New membership function for poverty measure," Metroeconomica, Wiley Blackwell, vol. 71(4), pages 676-688, November.
    5. World Bank, 2018. "Global Financial Development Report 2017/2018," World Bank Publications - Books, The World Bank Group, number 28482, December.
    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. Belhadj, Besma, 2023. "New fuzzy multiple regressions for the instantaneous and panel data “The determinants of Poverty in the Countries MENA”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    2. Giordani, Paolo & Kiers, Henk A. L., 2004. "Principal Component Analysis of symmetric fuzzy data," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 519-548, April.
    3. Margherita Casini & Francesca Gagliardi & Gianni Betti, 2018. "Sustainable Development Goals indicators: a methodological proposal for a fuzzy Super Index in the Mediterranean area," Department of Economics University of Siena 782, Department of Economics, University of Siena.
    4. D'Urso, Pierpaolo & Giordani, Paolo, 2003. "A least squares approach to Principal Component Analysis for interval valued data," Economics & Statistics Discussion Papers esdp03013, University of Molise, Department of Economics.
    5. Pierpaolo D’Urso & Marta Disegna & Riccardo Massari, 2020. "Satisfaction and Tourism Expenditure Behaviour," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 1081-1106, June.
    6. Chantal Mveh-Abia & Yves Emvudu & Eric Kokomo, 2018. "Measurement of the monetary poverty in Cameroon using fuzzy measure theory," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(4), pages 1-3.
    7. Vijay Verma & Gianni Betti & Francesca Gagliardi, 2017. "Fuzzy Measures of Longitudinal Poverty in a Comparative Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(2), pages 435-454, January.
    8. D'Urso, Pierpaolo & Giordani, Paolo, 2003. "A possibilistic approach to latent structure analysis for symmetric fuzzy data," Economics & Statistics Discussion Papers esdp03014, University of Molise, Department of Economics.
    9. Enrico Ciavolino & Antonio Calcagnì, 2014. "A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3401-3414, November.
    10. Besma Belhadj & Firas Kaabi, 2020. "New membership function for poverty measure," Metroeconomica, Wiley Blackwell, vol. 71(4), pages 676-688, November.
    11. D'Urso, Pierpaolo, 2003. "Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 47-72, February.
    12. Margherita Casini & Simone Bastianoni & Francesca Gagliardi & Massimo Gigliotti & Angelo Riccaboni & Gianni Betti, 2019. "Sustainable Development Goals Indicators: A Methodological Proposal for a Multidimensional Fuzzy Index in the Mediterranean Area," Sustainability, MDPI, vol. 11(4), pages 1-25, February.
    13. Wu, Hsien-Chung, 2003. "Fuzzy estimates of regression parameters in linear regression models for imprecise input and output data," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 203-217, February.
    14. Jin Hee Yoon & Przemyslaw Grzegorzewski, 2020. "On Optimal and Asymptotic Properties of a Fuzzy L 2 Estimator," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    15. Pierpaolo D’Urso & Riccardo Massari, 2013. "Weighted Least Squares and Least Median Squares estimation for the fuzzy linear regression analysis," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 279-306, November.
    16. Pierpaolo D’Urso & María Ángeles Gil, 2017. "Fuzzy data analysis and classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 645-657, December.
    17. Belhadj Besma, 2016. "Inequality among the poor in poverty measure case of Tunisia (2005–2010)," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 409-425, June.
    18. Antonio Terceño & María Glòria Barberà-Mariné & Yanina Laumann, 2018. "Análisis de los coeficientes beta: evidencia en el mercado de activos chileno," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 21(3), pages 076-093, December.
    19. Ana Colubi & Renato Coppi & Pierpaolo D’urso & Maria angeles Gil, 2007. "Statistics with fuzzy random variables," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 277-303.
    20. Coppi, Renato & D'Urso, Pierpaolo & Giordani, Paolo & Santoro, Adriana, 2006. "Least squares estimation of a linear regression model with LR fuzzy response," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 267-286, November.

    More about this item

    Keywords

    Fuzzy endogenous regressor; Fuzzy parameters; Fuzzy mathematical modeling; Cross-sectional data; Panel data;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

    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:soceps:v:91:y:2024:i:c:s0038012123002732. 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/seps .

    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.