Non-linear transformation tim-series models and causal analysis

本研究提供了一种刻画多变量间依赖结构的方法。针对平稳阿尔马模型，提出了一种可行的数值方法，对部分因果测度进行统计估计和检验。虽然该方法主要针对平稳的阿尔马模型，但在协整时间序列中具有广泛的适用性。本研究进行模拟研究，以评估小样本性能的开发插件估计方法的依赖性措施的基础上，大规模的Monte Carlo实验，也是三步最大Whittle似然估计程序的模型参数。该研究还有助于对美国宏观经济和金融经济进行实证研究。特别是对真实的GDP、M2和CPI的期限利差与增长率之间的因果结构进行了深入研究，为相关领域的文献增添了新的视角。

This research provides an approach to characterize the dependency structure between multivariate variables. Focused on the stationary ARMA model, the research produced a feasible way of numerically feasible method of conducting statistical estimation and testing of partial causal measures. Although focused on the stationary ARMA model, the approach has wide applicability in cointegrated time-series. The research conducted simulation study to evaluate the small-sample performance of the developed plug-in estimation method for the interdependence measures on the basis of large-scale Monte Carlo experiments and also the three-step maximum Whittle likelihood estimation procedure for the model parameters. The research also contributed in an empirical study on the U.S. macro and financial economy. In particular, intensive study was made on the causal structure among the term spread and growth rates of real GDP, M2 and CPI, adding a new insight to the literature of the allied field.