Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now

We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante. To improve forecasting accuracy, PRISM‐Now adaptively selects an aggregation quantile using contemporaneous ensemble distributional information, including changes in central tendency, dispersion, and skewness. Empirical results show that PRISM‐Now outperforms alternative ensemble methods, including simple averaging and approaches that rely on backward‐looking information. Using Korean GDP data, we further find that conventional models perform relatively well for nowcasting ( t+0) when near‐complete data are available, while big data and machine learning models exhibit stronger performance for one‐quarter‐ahead forecasts ( t+1) in the absence of realized information. Models incorporating text and sentiment data are particularly effective during the COVID‐19 period. Overall, these findings highlight the value of dynamic ensembling in adapting to rapidly changing economic conditions.