平稳和非平稳下时间序列的有限样本预测模型选择
Predictive, finite-sample model choice for time series under stationarity and non-stationarity
摘要：
在统计研究中，通常存在结构上更简单或更复杂的模型之间的选择。我们认为，即使一个更复杂的，本地平稳时间序列模型是真的，那么一个简单的，平稳时间序列模型可能是有利的工作参数不确定性。我们提出了一种新的模型选择方法，其中两个竞争的方法之一是选择基于其经验，有限样本的性能预测，在某种程度上，确保可解释性。一个严格的，理论分析的程序。作为一个重要的副作用，我们证明，可能发散模型的顺序，本地化的Yule-Walker估计是强，一致一致的局部平稳性。一个R包，forecastSNSTS，提供和使用的金融和气象数据的实证例子中应用的方法。我们进一步提供了一个广泛的模拟研究和讨论时，它是最好的基础上更不稳定的随时间变化的估计预测，当它是有利的预测，如果数据是从一个平稳过程，即使他们可能不是。
英文摘要：
In statistical research there usually exists a choice between structurally simpler or more complex models. We argue that, even if a more complex, locally stationary time series model were true, then a simple, stationary time series model may be advantageous to work with under parameter uncertainty. We present a new model choice methodology, where one of two competing approaches is chosen based on its empirical, finite-sample performance with respect to prediction, in a manner that ensures interpretability. A rigorous, theoretical analysis of the procedure is provided. As an important side result we prove, for possibly diverging model order, that the localised Yule-Walker estimator is strongly, uniformly consistent under local stationarity. An R package, forecastSNSTS, is provided and used to apply the methodology to financial and meteorological data in empirical examples. We further provide an extensive simulation study and discuss when it is preferable to base forecasts on the more volatile time-varying estimates and when it is advantageous to forecast as if the data were from a stationary process, even though they might not be.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
