Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that generalizes Gaussian ARMA and ARFIMA processes to allow both non-Gaussian marginal behaviour and a non-Gaussian description of the serial partial dependence structure. Extensions of classical causal and invertible representations of linear processes to general s-vine processes are proposed and investigated. A practical and parsimonious method for parameterizing s-vine processes using the Kendall partial autocorrelation function is developed. The potential of the resulting models to give improved statistical fits in many applications is indicated with an example using macroeconomic data.
翻译:可以使用基于两变相相交织功能组合的线性分解法来构建固定和电子时间序列。这种过程延伸至无限的相交序列被考虑和显示为产生大量模型,这些模型概括了Gaussian ARMA 和 ARFIMA 过程,以便允许非高加索边际行为和非高加索对序列部分依赖结构的描述。提出并调查了传统因果和线性线性过程对一般双变相过程的可视表达法的延伸。开发了使用Kendall 部分自动交火功能对S-value进程进行参数比较的实用和相似的方法。由此形成的模型有可能改善许多应用中的统计适用性,以宏观经济数据为例。