Abstract

We show that time-varying parameter state-space models estimated using the Kalman filter are particularly vulnerable to the problem of spurious regression, because the integrated error is transferred to the estimated state equation. We offer a simple yet effective methodology to reliably recover the instability in cointegrating vectors. In the process, the proposed methodology successfully distinguishes between the cases of no cointegration, fixed cointegration, and time-varying cointegration. We apply these proposed tests to elucidate the relationship between concentrations of greenhouse gases and global temperatures, an important relationship to both climate scientists and economists.

摘要

我们表明，使用卡尔曼滤波器估计的时变参数状态空间模型特别容易受到虚假回归问题的影响，因为积分误差被转移到估计的状态方程中。我们提供了一种简单而有效的方法来可靠地恢复协整向量的不稳定性。在此过程中，所提出的方法成功地区分了无协整、固定协整和时变协整的情况。我们应用这些提议的测试来阐明温室气体浓度与全球温度之间的关系，这对气候科学家和经济学家来说都是一个重要的关系。