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2013 | 13[28] | 4 |
Tytuł artykułu

Modelling joint distribution of crop plant yields and prices with use of a copula function

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EN
Abstrakty
EN
The paper constitutes an attempt at modelling the joint distribution of crop plant yields and prices in Poland. The main objective of the paper was to examine the usefulness of the copula function for the task and the selection of suitable marginal distributions. The fit of a joint distribution based copula function was compared with multivariate normal distribution. It was revealed that the multivariate normal distribution is outperformed by a Gaussian copula with the following marginal distribution: yields of both crop plants – normal distribution, price of wheat – Burr distribution (type XII) and price of rapeseeds – lognormal distribution. The main advantages of the copula function were: the possibility to use different marginal distributions and ability to model non-elliptical twodimensional distributions. The practical implications of choosing the right joint distribution is demonstrated by comparing empirical quantiles of income for a given crop structure with theoretical quantiles based on the proposed joint distributions.
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autor
  • Department of Agricultural Economics and International Economic Relations, Warsaw University of Life Sciences - SGGW, Warsaw, Poland
Bibliografia
  • Dutang C., Goulet V., Pigeon M. [2008]: actuar: An R Package for Actuarial Science. Journal of Statistical Software, vol. 25, no. 7, pp. 1-37. URL http://www.jstatsoft.org/v25/i07.
  • Hofert M., Kojadinovic I., Maechler M., Yan J., [2013]: copula: Multivariate Dependence with Copulas. R package version 0.999-7. URL http://CRAN.R-project.org/package=copula.
  • R Core Team [2013]: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
  • Schulte-Geers M., Berg E. [2011]: Modelling farm production risk with copulae instead of correlations. Paper prepared for presentation at the EAAE 2011 Congress Change and Uncertainty Challenges for Agriculture, Food and Natural Resources August 30 to September 2, Zurich.
  • Sklar A. [1959]: Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 8, pp. 229- 231.
  • Tadikamalla P. R. [1980]: A look at the Burr and related distributions. International Statistical Review, Vol. 48, Number 3, pp. 337–344.
  • Tejeda H.A., Goodwin B.K. [2008]: Modeling Crop prices through a Burr distribution and Analysis of Correlation between Crop Prices and Yields using a Copula method. Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Orlando, July 27-29.
  • Voung Q.H. [1989]: Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses. Econometrica, Vol. 57, No. 2, pp. 307-333.
  • Zhu Y., Ghosh S.K., Goodwin B.K. [2008]: Modeling Dependence in the Design of Whole Farm Insurance Contract, |A Copula-Based Model Approach. Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Orlando, July 27-29.
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PL
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Bibliografia
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