The Federal Reserve devotes significant resources to forecasting key economic variables such as real gross domestic product growth, employment, and inflation. The outlook for these variables also matters a great deal to businesses and financial market participants. The authors present a factor-augmented Bayesian vector autoregressive forecasting model that significantly outperforms both a benchmark random walk model and a pure time-series model. They then use these factors in an ordered probit model to develop the probability distribution over a 12-month horizon. One distribution assesses the probability that inflation will exceed 2.5 percent over the next year; they term this probability a price pressure measure. This price pressure measure would provide policymakers and markets with a quantitative assessment of the probability that average inflation over the next 12 months will be higher than the Fed’s long-term inflation target of 2 percent.