Real-Time Nowcasting of GDP: A Factor Model vs. Professional Forecasters
使用因子模型对美国GDP增长进行实时预测，基于1997至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。
type="main" xml:id="obes12047-abs-0001"> 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).