Econ Papers Daily / 工作论文
即时预测模型的附加特性：哪些重要及何时重要？
Bells and Whistles of Nowcasting Models: Which Matter and When?
摘要
This paper investigates which features of a Bayesian dynamic factor model improve U.S. GDP nowcasts and whether their contribution varies across historical episodes. Using pseudo-real-time information sets for 25 series, we estimate 16 combinations of dynamic heterogeneity, stochastic volatility, time-varying long-run growth, and a multiplicative outlier adjustment. We evaluate their marginal contributions to point, density, and quantile score forecasts using balanced paired factorial contrasts. Dynamic heterogeneity improves point, density, and tail forecasts, and it is the only feature that helps consistently across all three criteria. Stochastic volatility improves density and tail forecasts in some periods but not point forecasts consistently. Time-varying long-run growth lowers squared-error loss in some periods but worsens tail forecasts. The outlier adjustment does not improve point or density forecasts overall, has favorable but imprecisely estimated effects on upper-tail quantile scores during the pandemic, and worsens those scores afterward.
中文摘要
本文考察贝叶斯动态因子模型的哪些特征能够改进美国GDP的即时预测，以及这些特征的贡献是否在不同历史时期存在差异。利用25个序列的伪实时信息集，我们估计了动态异质性、随机波动率、时变长期增长以及乘法异常值调整的16种组合。我们使用平衡配对因子对比评估它们对点预测、密度预测和分位数得分预测的边际贡献。动态异质性改进了点预测、密度预测和尾部预测，并且是唯一在全部三项标准上均持续发挥作用的特征。随机波动率在某些时期改进了密度预测和尾部预测，但并未持续改进点预测。时变长期增长在某些时期降低了平方误差损失，但恶化了尾部预测。异常值调整总体上并未改进点预测或密度预测，在疫情期间对上尾分位数得分具有有利但估计不精确的影响，而在疫情之后则恶化了这些得分。