A New Model of Trend Inflation Using Disaggregates, Survey Expectations, and Uncertainty

This paper develops a new empirical model that estimates trend inflation by combining modeling features that have advanced the literature on trend inflation over the past two decades. These features include incorporating information about long-term inflation expectations from surveys in a flexible way, modeling aggregate inflation via sectoral data (goods and services), allowing for stochastic volatility (SV) in the shocks to the trend and transitory components of inflation, allowing for a time-varying price Phillips curve, and allowing for time-varying uncertainty effects on the level of inflation. We estimate the model using state-of-the-art Bayesian methods. We document the competitive properties of the new model compared to variants that include only a subset of the above features. The new model provides a more interpretable historical decomposition of inflation data than the models it extends. The decomposition suggests that uncertainty effects play a greater role than cyclical effects in explaining inflation fluctuations.