Nowcasting GDP in Real Time: A Density Combination Approach
利用美国实时数据，结合三类常用模型生成季度GDP增长的组合密度即时预测，发现新信息发布时预测对数得分几乎单调上升，且密度组合方法优于单一模型选择和点预测组合。
In this article, we use U.S. real-time data to produce combined density nowcasts of quarterly Gross Domestic Product (GDP) growth, using a system of three commonly used model classes. We update the density nowcast for every new data release throughout the quarter, and highlight the importance of new information for nowcasting. Our results show that the logarithmic score of the predictive densities for U.S. GDP growth increase almost monotonically, as new information arrives during the quarter. While the ranking of the model classes changes during the quarter, the combined density nowcasts always perform well relative to the model classes in terms of both logarithmic scores and calibration tests. The density combination approach is superior to a simple model selection strategy and also performs better in terms of point forecast evaluation than standard point forecast combinations.