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

Abstract

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.