Using Survey Information for Improving the Density Nowcasting of U.S. GDP
提出一种将统计即时预测模型与调查信息相结合的方法，通过对齐预测分布的一阶和二阶矩来改进美国实际GDP的密度预测，发现调查信息在极端事件和尾部行为捕捉中具有重要价值。
We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants’ predictions, often used as a measure of “ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.