论文标题
加权减少等级估计量在协整等级不确定性下
Weighted Reduced Rank Estimators Under Cointegration Rank Uncertainty
论文作者
论文摘要
为非平稳线性过程开发了协整分析，这些过程在坐标之间表现出固定关系。多维协整过程中协整关系的估计通常分为两个步骤进行。首先是估计等级的，然后根据估计等级（降低等级回归）进行估算的协整矩阵。估计量的渐近学通常是在知道真实等级的假设下得出的。在本文中，我们量化了渐近偏差，并在缩小范围内发现协整估计量的渐近分布。此外，我们建议一类新的加权等级估计量，该类别可以在排名选择很难的设置中提高灵活性。我们从经验上表明，当存在不确定性时，适当的权重选择会导致预测性能提高。最后，我们从视觉处理的心理实验中说明了经验脑电图数据的估计量。
Cointegration analysis was developed for non-stationary linear processes that exhibit stationary relationships between coordinates. Estimation of the cointegration relationships in a multi-dimensional cointegrated process typically proceeds in two steps. First the rank is estimated, then the cointegration matrix is estimated, conditionally on the estimated rank (reduced rank regression). The asymptotics of the estimator is usually derived under the assumption of knowing the true rank. In this paper, we quantify the asymptotic bias and find the asymptotic distributions of the cointegration estimator in case of misspecified rank. Furthermore, we suggest a new class of weighted reduced rank estimators that allow for more flexibility in settings where rank selection is hard. We show empirically that a proper choice of weights can lead to increased predictive performance when there is rank uncertainty. Finally, we illustrate the estimators on empirical EEG data from a psychological experiment on visual processing.
