Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) and dynamic factor models (DFMs) are the two main state-of-the-art approaches to modeling series with non-homogeneous frequencies. We introduce a new framework, called the multi-frequency echo state network (MFESN), based on a relatively novel machine learning paradigm called reservoir computing. Echo state networks (ESNs) are recurrent neural networks formulated as nonlinear state-space systems with random state coefficients where only the observation map is subject to estimation. MFESNs are considerably more efficient than DFMs and can incorporate many series, as opposed to MIDAS models, which are prone to the curse of dimensionality. All methods are compared in extensive multistep forecasting exercises targeting U.S. GDP growth. We find that our MFESN models achieve superior or comparable performance over MIDAS and DFMs at a much lower computational cost.

宏观经济预测最近开始采用可以处理发布周期不等的大规模数据集和序列的技术。混合数据采样 (MIDAS) 和动态因子模型 (DFM) 是对非均匀频率序列进行建模的两种主要最先进方法。我们引入了一种新的框架，称为多频回声状态网络（MFESN），它基于一种相对新颖的机器学习范式（称为储层计算）。回波状态网络 (ESN) 是递归神经网络，被表述为具有随机状态系数的非线性状态空间系统，其中仅对观测图进行估计。MFESN 比 DFM 效率高得多，并且可以包含许多级数，这与容易受到维数灾难的 MIDAS 模型不同。所有方法都在针对美国 GDP 增长的广泛多步预测练习中进行比较。我们发现我们的 MFESN 模型以低得多的计算成本实现了优于 MIDAS 和 DFM 的性能或相当的性能。