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对于pair-copula中的参数估计,大多假设copula函数的参数和条件变量独立,将参数简化成一个不依赖于条件变量的常数。本文假设copula函数的参数和条件变量不独立,该参数是以条件变量为自变量的一元函数。应用该方法实证分析了“克强指数”三个指标铁路货运量、工业用电量和贷款发放量的对数增长率之间的关系,研究发现该方法优于简化的pair-copula参数估计,并且得出在固定铁路货运量不变时,工业用电量和银行贷款发放量成负相关关系,且这种负相关性随铁路货运量增加而减弱。
For the parameter estimation in pair-copula, it is mostly assumed that the parameters and conditional variables of the copula function are independent, and the parameters are reduced to a constant that does not depend on the condition variable. This article assumes that copula function parameters and conditional variables are not independent, the parameter is a conditional variable as an argument to a unary function. Employing this method, this paper empirically analyzes the relationship between the logarithmic growth rate of rail freight volume, industrial electricity consumption and loan issuance for the three indexes of “Keqiang Index”, and finds that this method is superior to the simplified pair-copula parameter It is also concluded that there is a negative correlation between industrial electricity consumption and bank loan issuance when the freight volume of fixed railways is unchanged, and this negative correlation is weakened with the increase of rail freight volume.