论文标题
高维协整系统
High-dimensional cointegration and Kuramoto systems
论文作者
论文摘要
本文提出了一个新的估计量，该估计量在高维协调过程的背景下对对称性和低等级的非标准限制。此外，我们通过限制了高斯创新的自举，讨论了高维协调过程的等级估计。我们证明，协整系统的经典等级测试容易低估真正的等级，并在100维系统中证明这种效果。我们还讨论了这种低估对这种高维系统的含义。此外，我们定义了一个线性化的库拉托托系统，并进行了模拟研究，在其中推断了不受限制的$ p \ p \ times p $系统的协整等级，并基于图形方法和对称性的低级估计器的基础群集网络结构的依次依次，该估计值是从这种无效约束下的couparametrization reparametrization reparametrized得出的。
This paper presents a novel estimator for a non-standard restriction to both symmetry and low rank in the context of high dimensional cointegrated processes. Furthermore, we discuss rank estimation for high dimensional cointegrated processes by restricted bootstrapping of the Gaussian innovations. We demonstrate that the classical rank test for cointegrated systems is prone to underestimate the true rank and demonstrate this effect in a 100 dimensional system. We also discuss the implications of this underestimation for such high dimensional systems in general. Also, we define a linearized Kuramoto system and present a simulation study, where we infer the cointegration rank of the unrestricted $p\times p$ system and successively the underlying clustered network structure based on a graphical approach and a symmetrized low rank estimator of the couplings derived from a reparametrization of the likelihood under this unusual restriction.
