Combined Density Nowcasting in an Uncertain Economic Environment

We introduce a combined density nowcasting (CDN) approach to dynamic factor models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian sequential Monte Carlo method which rebalances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on U.S. real-time data of 120 monthly variables, indicate that CDN gives more accurate density nowcasts of U.S. GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.

本文提出一种面向动态因子模型（dynamic factor models, DFM）的联合密度现在预测（combined density nowcasting, CDN）方法，该方法可通过统一框架兼顾多种模型与数据特征的时变不确定性，进而生成更为精准且完备的密度现在预测结果。该组合权重为隐随机变量，其取值依赖于过往现在预测表现与其他学习机制。此联合密度框架被嵌入贝叶斯序贯蒙特卡洛方法，该方法会基于时变权重的更新信息，在每个周期内对当期的现在预测密度集合进行重新平衡。模拟数据实验结果表明，在数据发布较早、数据不确定性较高且模型存在不完备性的场景下，CDN的表现尤为出色。基于120个月度变量的美国实时数据开展的实证研究显示，在整个季度周期内，相较于模型选择策略与其他组合策略，CDN能够生成更为精准的美国GDP增速密度现在预测结果，且在该季度前两个月的性能提升尤为显著。此外，CDN还能在近期经济衰退期间，为模型不完备性提供具有信息量的信号。聚焦于尾部风险维度，CDN可输出GDP负增长概率，该概率能够为实时识别经济衰退、判定经济疲软的结束时点提供可靠的预警信号。