Extracting Information from Different Expectations

Long-term expectations are believed to play a crucial role in driving future inflation and guiding monetary policy responses. However, expectations are not directly observed and the available measures can present a wide range of results. To understand what drives these differences, we examine the evolution of alternative consumer price inflation expectations in the United States between 2003-2019. We show that inflation forecasts can be improved by incorporating the differential between survey and market-based measures of expectations. Next, we decompose and extract the differentials in rigidity and information between measures of expectations. While both information and rigidities play a role, the information differential is more important. Using machine learning methods, we find that up to half of the information differential is explained by real-time changes in measures of liquidity. This also explains some past forecast improvements and helps predict the divergence in long-term inflation expectations in 2020.