Data-driven local polynomial for the trend and its derivatives in economic time series
Data-driven local polynomial for the trend and its derivatives in economic time series
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DOI：
10.1080/10485252.2020.1759598
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发表时间：
2020-04
影响因子：
1.2
通讯作者：
Yuanhua Feng;T. Gries;Marlon Fritz
中科院分区：
文献类型：
--
作者：
Yuanhua Feng;T. Gries;Marlon Fritz
摘要本文的主要目的是发展的数据驱动的迭代插件算法的局部多项式估计的趋势及其衍生物的相关误差。此外，数据驱动的滞后窗口估计的方差因子的带宽，使非参数阶段进行没有任何参数假设的平稳误差。进一步讨论了利用阿尔马模型进行残差分析的问题。此外，数据驱动算法的一些计算特性进行了详细的讨论。模拟研究和比较研究证实了这些建议的实际效果，并通过美国季度GDP和劳动力数据加以说明。基于本文的建议，开发了一个用于平滑短记忆时间序列中趋势及其导数的R软件包，称为“smoots”（平滑时间序列）。
ABSTRACT The main purpose of this paper is the development of data-driven iterative plug-in algorithms for local polynomial estimation of the trend and its derivatives under dependent errors. Furthermore, a data-driven lag-window estimator for the variance factor in the bandwidth is proposed so that the nonparametric stage is carried out without any parametric assumption on the stationary errors. Analysis of the residuals using an ARMA model is further discussed. Moreover, some computational features of the data-driven algorithms are discussed in detail. Practical performance of the proposals is confirmed by a simulation study and a comparative study, and illustrated by quarterly US GDP and labour force data. An R package called ‘smoots’ (smoothing time series) for smoothing the trend and its derivatives in short-memory time series is developed based on the proposals of this paper.