In this paper we survey a number of recent empirical findings regarding the usefulness of including diffusion indexes in dynamic Nelson-Siegel (DNS) type models used to predict the term structure of interest rates (see e.g., Diebold and Li (2007) and Diebold and Rudebusch (2013)). We also survey various empirical methods used in the construction of DNS models, and used to specify and estimate diffusion index augmented DNS models. In particular, we review (sparse) principal component analysis, factor augmented autoregression, and various dimension reduction, variable selection, machine learning, and shrinkage methods, such as the least absolute shrinkage operator (lasso), the elastic net, and independent component analysis, among others. Finally, we discuss the importance of using real-time data in contexts where datasets are subject to revision; and we compare and contrast the use of targeted versus un-targeted specification methods when including diffusion indexes in DNS type prediction models. Interestingly, as noted in Swanson and Xiong (2018a, 2018b), the usefulness of diffusion indexes is crucially dependent upon whether real-time data are used or not. Specifically, when real-time data are used to estimate the weights in di usion indexes, it is found that relatively few “data rich” models that use big data are preferred to simpler DNS models, post 2010. Instead, pure DNS models that rely only on historical interest rate data deliver mean square error “best” forecasts. However, when data are not real-time, diffusion indexes always have marginal predictive content for interest rates. Moreover, it is clear that in more volatile interest rate regimes, such as prior to 2010, machine learning and related methods have much to offer, regardless of the type of dataset used in their construction.

中文翻译：

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动态Nelson-Siegel模型，扩散指数和大数据方法的预测利率调查

在本文中，我们调查了许多有关将扩散指数包括在动态Nelson-Siegel（DNS）类型模型中以预测利率期限结构的有用性的最新经验发现（例如，参见Diebold和Li（2007）以及Diebold和Rudebusch（2013））。我们还调查了DNS模型构建中使用的各种经验方法，并用于指定和估计扩散指数增强的DNS模型。特别是，我们审查（稀疏）主成分分析，因子增强自回归以及各种降维，变量选择，机器学习和收缩方法，例如最小绝对收缩算子（lasso），弹性网和独立成分分析等等。最后，我们讨论了在数据集需要修订的情况下使用实时数据的重要性。当在DNS类型预测模型中包含扩散索引时，我们将有针对性的规范方法与无目标的规范方法进行了比较和对比。有趣的是，正如Swanson和Xiong（2018a，2018b）所指出的那样，扩散指数的有用性关键取决于是否使用实时数据。具体来说，当使用实时数据估算维度索引中的权重时，发现相对较少的使用大数据的“数据丰富”模型比简单的DNS模型（2010年后）更受青睐。相反，纯DNS模型依赖仅基于历史利率数据才能提供均方误差“最佳”预测。但是，当数据不是实时数据时，扩散指数始终对利率具有边际预测性内容。此外，很明显，在利率波动较大的情况下（例如2010年之前），