COPULA MODELING IN ANALYSIS OF DEPENDENCY OF OIL PALM PRODUCTION AND RAINFALL
DOI:
https://doi.org/10.24246/ijpna.v3i3.89-94Keywords:
Relationship, Correlation coefficient, Archimedean Copula, Elliptical Copula, Maximum Likelihood EstimationAbstract
Copula is a method that examines the relationship pattern between variables. Copula is characterized as a nonparametric method with several benefits, i.e., it is independent of the assumption of the distribution, accommodates nonlinear relationship among variables, and is convenient in building joint distribution. This study investigates the relationship and prediction analysis using the copula approach. The method is applied to the monthly data of oil palm production and the amount of rainfall. The results show that the model of Frank Copula is the best model for rainfall and oil palm production relationship.
Downloads
References
Anisa, KN, Sutikno (2015). Analysis of relationship of rainfall and El-Nino Southern Oscillation indicator in center production of rice in East Java with Copula approach. Jurnal Sains dan Seni ITS, 4(1): 49-54. (in Bahasa Indonesia).
Embrechts P, Lindskog F, McNeil A (2001). Modeling dependence with Copulas and applications to risk management. In: Rachev ST (ed), Handbook of heavy tailed distributions in finance (pp. 22-26). San Diego: Elsevier.
Prasetyo, EI (2009). Analysis of relationship of rainfall and oil palm production with transfer function model [thesis]. Bogor: Institut Pertanian Bogor. (In Bahasa Indonesia)
Genest C, Nešlehová J (2010). Copulas: Introduction to the theory and implementation in R with applications in finance and insurance. Course material for the workshop presented at the 38th Annual Meeting of the Statistical Society of Canada, Quebec (QC), May 23, 2010.
Murteira JM, Lourenço ÓD (2007). Health care utilization and self-assessed health: Specification of bivariate models using Copulas. Health, Econometrics and Data Group. The University of York. ISSN 1751-1976.
Risza S (2009). Oil Palm - an effort to increase productivity. Yogyakarta: Kanisius. (in Bahasa Indonesia).
Schoelzel C, Friederichs P (2008). Multivariate non-normally distributed random variables in climate research – Introduction to The Copula Approach. Nonlin. Processes Geophys, 15: 761–772.
Simanjuntak LN, Sipayung R, Irsal (2014). Influence of rainfall and rainy days on the production of oil palm aged 5, 10 and 15 years at the Garden Begerpang Estate PT. PP London Sumatra Indonesia, Tbk. Jurnal Online Agroteknologi, 2: 1141-1151. (in Bahasa Indonesia).
Syahrir I (2011). Estimation of Copula parameter and its application in climatology. Surabaya: TS. (in Bahasa Indonesia).
Udayani LG, Sumarjaya IW, Susilawati M (2016). Analysis of relationship of rice production and ENSO indicator in Tabanan District with Copula approach. E-Jurnal Matematika, 5: 164-169. (in Bahasa Indonesia).
Wang A (2010). Goodness-of-Fit Tests for Archimedean Copula Models. Statistica Sinica, 20: 441-453.
Zhu Y, Ghosh SK, Goodwin BK (2008). Modeling dependence in the design of whole insurance contract - A Copula - based model approach. Selected Paper for presentation at the American Agricultural Economics Association Annual Meeting, Orlando, Florida, July 27-29, 2008.
Downloads
Published
How to Cite
Issue
Section
License
Indonesian Journal of Physics and Nuclear Applications is licensed under a Creative Commons Attribution 4.0 International License.
