Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?

Abstract

This paper empirically investigates two alternative combination strategies, namely forecast combination and information pooling, in the context of nowcasting French GDP in real time with monthly survey opinions. According to the encompassing paradigm, we claim that the outperformance of the forecast combination strategy reported by recent works may be related to the issues of model selection and misspecification. To address these issues, we promote the blocking modeling approach to allow us to handle mixed frequencies in a linear framework that is compatible with an automatic model selection algorithm. Selected restricted- and pooled-information models are specified and tested for forecast encompassing in order to determine the best combination strategy. The results suggest that the forecast combination strategy dominates as long as no individual (restricted) model encompasses the rivals. However, when a predictive encompassing model is obtained by pooling the information sets, this model outperforms the most accurate forecast combination scheme.