Based on evidence gathered from a newly built large macroeconomic dataset (MD) for the UK, labelled UK-MD and comparable to similar datasets for the United States and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.

中文翻译：

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机器学习能否赶上 COVID-19 经济衰退？

根据从英国新建的大型宏观经济数据集 (MD) 中收集的证据，标记为 UK-MD 并且与美国和加拿大的类似数据集相当，在大流行期间进行预测的最有希望的途径似乎是允许一般通过使用机器学习 (ML) 方法的非线性形式。但并非所有非线性 ML 方法都是相似的。例如，有些不允许外推（如常规树木和森林），有些则允许（当补充线性动态组件时）。在空前的时代，基于 ML 的预测的这个和其他关键方面在广泛的伪样本外练习中进行了研究。