Real-time density nowcasts of US inflation: A model combination approach

Abstract

We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. These features provide density nowcasts that can potentially accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our framework using high-frequency real-time data over the period 2000–2015.