IDEAS home Printed from https://ideas.repec.org/p/ags/aaea20/304313.html
   My bibliography  Save this paper

The dependence structure between yields and prices: A copula-based model of French farm income

Author

Listed:
  • Bousebata, Meryem
  • Enjolras, Geoffroy
  • Girard, Stéphane

Abstract

No abstract is available for this item.

Suggested Citation

  • Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2020. "The dependence structure between yields and prices: A copula-based model of French farm income," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304313, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea20:304313
    DOI: 10.22004/ag.econ.304313
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/304313/files/18229.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.304313?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. Nadja El Benni & Robert Finger & Miranda P.M. Meuwissen, 2016. "Potential effects of the income stabilisation tool (IST) in Swiss agriculture," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(3), pages 475-502.
    2. Christos J. Emmanouilides & Panos Fousekis, 2015. "Vertical price dependence structures: copula-based evidence from the beef supply chain in the USA," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 42(1), pages 77-97.
    3. Tamara Ben-Ari & Julien Boé & Philippe Ciais & Remi Lecerf & Marijn Van der Velde & David Makowski, 2018. "Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    4. Gijbels, Irène & Veraverbeke, Noël & Omelka, Marel, 2011. "Conditional copulas, association measures and their applications," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1919-1932, May.
    5. Jean-Paul Chavas, 2011. "Agricultural policy in an uncertain world," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 38(3), pages 383-407, August.
    6. D. Gale Johnson, 1975. "World Agriculture, Commodity Policy, and Price Variability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 57(5), pages 823-828.
    7. Zhu, Ying & Ghosh, Sujit K. & Goodwin, Barry K., 2008. "Modeling Dependence in the Design of Whole Farm---A Copula-Based Model Approach," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6282, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. Sherrick, Bruce, 2012. "Relative Importance of Price vs. Yield variability in Crop Revenue Risk," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 2, October.
    9. Panos Fousekis & Vasilis Grigoriadis, 2017. "Joint price dynamics of quality differentiated commodities: copula evidence from coffee varieties," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 44(2), pages 337-358.
    10. Barry K. Goodwin & Ashley Hungerford, 2015. "Copula-Based Models of Systemic Risk in U.S. Agriculture: Implications for Crop Insurance and Reinsurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(3), pages 879-896.
    11. Noël Veraverbeke & Marek Omelka & Irène Gijbels, 2011. "Estimation of a Conditional Copula and Association Measures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(4), pages 766-780, 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. Gijbels, Irène & Sznajder, Dominik, 2013. "Testing tail monotonicity by constrained copula estimation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 338-351.
    2. Miao, Ruiqing & Hennessy, David A. & Feng, Hongli, 2016. "The Effects of Crop Insurance Subsidies and Sodsaver on Land-Use Change," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(2), May.
    3. Dimitrios Panagiotou & Athanassios Stavrakoudis, 2023. "Price dependence among the major EU extra virgin olive oil markets: a time scale analysis," Review of Agricultural, Food and Environmental Studies, Springer, vol. 104(1), pages 1-26, March.
    4. Derumigny Alexis & Fermanian Jean-David, 2017. "About tests of the “simplifying” assumption for conditional copulas," Dependence Modeling, De Gruyter, vol. 5(1), pages 154-197, August.
    5. Jonas Meier, 2020. "Multivariate Distribution Regression," Diskussionsschriften dp2023, Universitaet Bern, Departement Volkswirtschaft.
    6. Robert Finger & Nadja El Benni, 2021. "Farm income in European agriculture: new perspectives on measurement and implications for policy evaluation," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(2), pages 253-265.
    7. Qi Liu & Chun Li & Valentine Wanga & Bryan E. Shepherd, 2018. "Covariate†adjusted Spearman's rank correlation with probability†scale residuals," Biometrics, The International Biometric Society, vol. 74(2), pages 595-605, June.
    8. Marra, Giampiero & Radice, Rosalba, 2017. "Bivariate copula additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 99-113.
    9. Grazian, Clara & Dalla Valle, Luciana & Liseo, Brunero, 2022. "Approximate Bayesian conditional copulas," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    10. Gardes, Laurent & Girard, Stéphane, 2015. "Nonparametric estimation of the conditional tail copula," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 1-16.
    11. Bianchi, Pascal & Elgui, Kevin & Portier, François, 2023. "Conditional independence testing via weighted partial copulas," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    12. Gijbels, Irène & Omelka, Marek & Pešta, Michal & Veraverbeke, Noël, 2017. "Score tests for covariate effects in conditional copulas," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 111-133.
    13. Nurudeen A. Adegoke & Andrew Punnett & Marti J. Anderson, 2022. "Estimation of Multivariate Dependence Structures via Constrained Maximum Likelihood," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 240-260, June.
    14. EnDer Su, 2018. "Measuring contagion risk in high volatility state among Taiwanese major banks," Risk Management, Palgrave Macmillan, vol. 20(3), pages 185-241, August.
    15. Jean-François Plante, 2017. "Rank correlation under categorical confounding," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-19, December.
    16. Jean-David Fermanian & Olivier Lopez, 2015. "Single-index copulae," Working Papers 2015-12, Center for Research in Economics and Statistics.
    17. Abegaz, Fentaw & Gijbels, Irène & Veraverbeke, Noël, 2012. "Semiparametric estimation of conditional copulas," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 43-73.
    18. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
    19. Seyyed Ali Zeytoon Nejad Moosavian & Barry K. Goodwin, 2021. "Flexible modelling of multivariate risks in pricing margin protection insurance: modelling portfolio risks with mixtures of mixtures," Applied Economics, Taylor & Francis Journals, vol. 53(4), pages 411-440, January.
    20. Alexis Derumigny & Jean-David Fermanian, 2018. "About Kendall's regression," Working Papers 2018-01, Center for Research in Economics and Statistics.

    More about this item

    Keywords

    Risk and Uncertainty; Research Methods/Statistical Methods; Agricultural Finance;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ags:aaea20:304313. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.