We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed-frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track using a specific form of intercept correction. Among these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and subsequent recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment, and very persistent negative effects on trend growth. Similar findings also emerge for forecasts by institutions, for survey forecasts, and for the other G7 countries.

我们考虑了一些简单的方法来改进在 Covid-19 危机和复苏期间通过具有各种指标的混合频率 MIDAS 和 UMIDAS 模型获得的增长临近预报和预测，例如将同一模型的各种规格的预测和/或跨不同的模型，通过添加 MA 项扩展模型规范，通过采用相似性方法增强估计方法，并使用特定形式的截距校正调整预测以使其回到正轨。在这些方法中，尽管金融危机的来源和特征不同，但将原始的临近预报和预测调整为与金融危机和随后复苏期间的临近预报和预测误差相似的量似乎对美国产生了最好的结果。特别是，调整后的 2020Q1 增长临近预测值更接近实际值，基于替代指标的调整后预测变得更加相似，不幸的是，所有这些都表明复苏比没有调整时要慢得多，对趋势增长的负面影响非常持久。机构预测、调查预测和其他 G7 国家的预测也出现了类似的结果。