论文标题
使用混合频率数据的宏观经济预测的储层计算
Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
论文作者
论文摘要
宏观经济的预测最近已经开始接受可以处理大型数据集和具有不平等发行期的系列的技术。混合数据采样（MIDAS）和动态因子模型（DFM）是允许具有非均匀频率的建模序列的两种主要最新方法。我们介绍了一个基于一个称为储层计算的相对新颖的机器学习范式的新框架，称为多频回波状态网络（MFESN）。回声状态网络（ESN）是反复的神经网络，该神经网络是随机状态系数的非线性状态空间系统，只有观察图进行估计。 MFESN比DFMS高得多，并且允许合并许多系列，而不是MIDAS模型，而MIDAS模型容易受到维数的诅咒。在针对美国GDP增长的广泛预测练习中，将所有方法比较所有方法。我们发现，我们的MFESN模型以比MIDAS和DFMS的计算成本低得多。
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 (DFM) are the two main state-of-the-art approaches that allow 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 (ESN) 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 allow for incorporating 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 US GDP growth. We find that our MFESN models achieve superior or comparable performance over MIDAS and DFMs at a much lower computational cost.
