Testing big data in a big crisis: Nowcasting under Covid-19
该研究为新冠疫情等非常规时期构建了包含上千个时间序列的欧洲宏观经济预测数据集，并提出一种动态贝叶斯框架来整合海量数据与多种预测方法，通过“选择先验”筛选有效变量，用于即时预测GDP。
During the COVID-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional times. The first innovation is the construction of an extensive data set for macroeconomic forecasting in Europe. We collect more than a thousand time series from conventional and unconventional sources, complementing traditional macroeconomic variables with timely big data indicators and assessing their added value at nowcasting. The second novelty consists of a methodology to merge an enormous amount of non-encompassing data with a large battery of classical and more sophisticated forecasting methods in a seamlessly dynamic Bayesian framework. Specifically, we introduce an innovative "selection prior" that is used not as a way to influence model outcomes, but as a selecting device among competing models. By applying this methodology to the COVID-19 crisis, we show which variables are good predictors for nowcasting Gross Domestic Product and draw lessons for dealing with possible future crises.