论文标题
循环奇异频谱分析：一种新的自动化过程，用于信号提取
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction
论文作者
论文摘要
有时，挑出与给定频率相关的波动是有意思的。我们提出了一种新的SSA，循环SSA（CISSA）的新变体，该变体允许提取与指定的任何频率相关的信号。与需要识别与提取信号相关的频率的其他程序相比，这是一种新颖性。我们证明CISSA渐近地等同于这些替代程序，尽管有优势避免了需要随后的频率识别。我们通过几个线性和非线性时间序列的模拟来检查其良好的性能，并将其与替代SSA方法进行比较。我们还证明了它在非组织情况下的有效性。为了展示它如何与实际数据一起工作，我们使用CISSA来提取商业周期并将六个国家的工业生产指数进行估算。经济学家遵循此指标，以实时评估经济状况。我们发现，估计的周期与经合组织的过时衰退相匹配，显示出其对商业周期分析的可靠性。最后，我们分析了估计组件的强可分离性。特别是，我们检查了季节性的时间序列没有显示任何残留季节性的证据。
Sometimes, it is of interest to single out the fluctuations associated to a given frequency. We propose a new variant of SSA, Circulant SSA (CiSSA), that allows to extract the signal associated to any frequency specified beforehand. This is a novelty when compared with other procedures that need to identify ex-post the frequencies associated to extracted signals. We prove that CiSSA is asymptotically equivalent to these alternative procedures although with the advantage of avoiding the need of the subsequent frequency identification. We check its good performance and compare it to alternative SSA methods through several simulations for linear and nonlinear time series. We also prove its validity in the nonstationary case. To show how it works with real data, we apply CiSSA to extract the business cycle and deseasonalize the Industrial Production Index of six countries. Economists follow this indicator in order to assess the state of the economy in real time. We find that the estimated cycles match the dated recessions from the OECD showing its reliability for business cycle analysis. Finally, we analyze the strong separability of the estimated components. In particular, we check that the deseasonalized time series do not show any evidence of residual seasonality.
