Modelling temperature measurement data by using copula functions

Abstract

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