分数阶协整子空间的半参数估计

Willa W. Chen, C. Hurvich
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引用次数: 60

摘要

我们考虑了一种多元分数协整的公共分量模型,其中约1个分量具有不同的记忆参数。协整秩允许大于1。真正的协整向量可以分解成正交的分数级协整子空间,使得来自不同子空间的向量产生具有不同内存参数的协整误差,当k = 1时,用dk表示;:::;5 .我们使用锥形、差分观测值的平均周期图矩阵的适当特征向量集分别估计每个协整子空间。平均使用前m个傅里叶频率,m固定。我们将证明,在第k个估计的协整子空间中的任何向量,在高概率下,接近于第k个真协整子空间,在这个意义上,估计的协整向量和真协整子空间之间的角在概率上收敛于零。这个角度是Op(ni®k),其中n是样本量,®k是对应于给定子空间和相邻子空间的存储器参数之间的最短距离。我们表明,与估计的协整向量对应的协整残差可用于获得给定协整子空间的内存参数的一致和渐近正态估计,使用带宽倾向于1比n慢的单变量高斯半参数估计器。我们还展示了如何使用这些内存参数估计来测试分数协整和一致地识别协整子空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semiparametric Estimation of Fractional Cointegrating Subspaces
We consider a common components model for multivariate fractional cointegration, in which the s ¸ 1 components have different memory parameters. The cointegrating rank is allowed to exceed 1. The true cointegrating vectors can be decomposed into orthogonal fractional cointegrating subspaces suchthat vectors from distinct subspaces yield cointegrating errors with distinct memory parameters, denoted by dk, for k = 1; : : : ; s. We estimate each cointegrating subspace separately using appropriate sets ofeigenvectors of an averaged periodogram matrix of tapered, differenced observations. The averaging uses the first m Fourier frequencies, with m fixed. We will show that any vector in the k th estimatedcointegrating subspace is, with high probability, close to the k th true cointegrating subspace, in the sensethat the angle between the estimated cointegrating vector and the true cointegrating subspace convergesin probability to zero. This angle is Op(ni®k ), where n is the sample size and ®k is the shortest distance between the memory parameters corresponding to the given and adjacent subspaces. We show that the cointegrating residuals corresponding to an estimated cointegrating vector can be used to obtain a consistent and asymptotically normal estimate of the memory parameter for the given cointegrating subspace, using a univariate Gaussian semiparametric estimator with a bandwidth that tends to 1 more slowly than n. We also show how these memory parameter estimates can be used to test for fractional cointegration and to consistently identify the cointegrating subspaces.
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