Real‐Time Nowcasting of GDP: A Factor Model vs. Professional Forecasters
使用因子模型对美国GDP增长进行实时即时预测，利用1997-2010年的实时数据库，发现模型精度随信息发布提高，且表现优于专业预测者调查。
Abstract We perform a fully real‐time nowcasting (forecasting) exercise of US GDP growth using Giannone et al .'s (2008) factor model framework. To this end, we have constructed a real‐time database of vintages from 1997 to 2010 for a panel of variables, enabling us to reproduce, for any given day in that range, the exact information that was available to a real‐time forecaster. We track the daily evolution of the model performance along the real‐time data flow and find that the precision of the nowcasts increases with information releases and the model fares well relative to the Survey of Professional Forecasters (SPF).