Modelling temperature measurement data by using copula functions

Abstract

In this study, methods of copula estimation are used and the temperature measurement data of thefour regions located at the same positions in the range of 01.01.2008 - 30.04.2009 was modeledwith copula functions. For dependence structures of the data sets, it is calculated Kendall Tau andSpearman Rho values which are nonparametric. Based on this method, parameters of copula areobtained. A clear advantage of the copula-based model is that it allows for maximum-likelihoodestimation using all available data. The main aim of the method is to find the parameters that makethe likelihood functions get its maximum value. With the help of the maximum-likelihood estimationmethod, for copula families, it is obtained likelihood values. These values, Akaike informationcriteria (AIC) are used to determine which copula supplies the suitability for the data set.