Forecasting future inflation and nowcasting contemporaneous inflation are difficult. We propose a new and parsimonious model for nowcasting headline and core inflation in the U.S. consumer price index and price index for personal consumption expenditures that relies on relatively few variables. The model's nowcasting accuracy improves as information accumulates over a month or quarter, outperforming statistical benchmarks. In real‐time comparisons, the model's headline inflation nowcasts substantially outperform those from the Blue Chip consensus and the Survey of Professional Forecasters. Across all four inflation measures, the model's nowcasting accuracy is comparable to that of the Federal Reserve Board's Greenbook.

We take a closer look at the connections between wages, prices, and economic activity. We find that causal relationships between wages and prices are difficult to identify, and the ability of wages to help predict future inflation is limited. Wages appear to be useful in assessing the current state of labor markets, but they are not necessarily sufficient for thinking about where the economy and inflation are going.

This paper constructs hybrid forecasts that combine both short-and longterm conditioning information from external surveys with forecasts from a standard fi xed-coeffi cient vector autoregression (VAR) model. Specifi cally, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of variables, including those that are not directly tilted but are affected through spillover effects from tilted variables. The forecast accuracy gains for infl ation are substantial, statistically signifi cant, and are competitive with the forecast accuracy from both time-varying VARs and univariate benchmarks. We view our proposal as an indirect approach to accommodating structural change and moving end points.

We estimate an empirical model of infl ation that exploits a Phillips curve relationship between a measure of unemployment and a subaggregate measure of infl ation (services). We generate an aggregate infl ation forecast from forecasts of the goods subcomponent separate from the services subcomponent, and compare the aggregated forecast to the leading time-series univariate and standard Phillips curve forecasting models. Our results indicate notable improvements in forecasting accuracy statistics for models that exploit relationships between services infl ation and the unemployment rate. In addition, models of services infl ation using the short-term unemployment rate (less than 27 weeks) as the real economic indicator display additional modest forecast accuracy improvements.