By accounting for differences between factors affecting prices for goods and services, a composite forecast model may be better able to predict core inflation than standard models, according to a new study from the Federal Reserve Bank of New York.
In “The Parts Are More Than the Whole: Separating Goods and Services to Predict Core Inflation,” authors Richard Peach, Robert Rich and M. Henry Linder adopt a new approach to forecasting core inflation based on the theory that growth rates for goods and services prices are shaped by different forces. While core services inflation depends on long-run inflation expectations and the degree of slack in the labor market, core goods inflation depends on short-run inflation expectations and import prices. By distinguishing these factors, the authors are able to generate more accurate forecasts from a composite model that combines separate forecasts for core goods inflation and core services inflation.
Core inflation—an inflation measure that excludes volatile food and energy prices—is used by policymakers to help gauge the economy’s underlying rate of inflation. However, standard Phillips curve models developed to forecast core inflation do not have a particularly good track record. The composite model presented in this paper improves upon the inflation forecasts generated by this standard model.
Richard Peach is a senior vice president, Robert Rich an assistant vice president, and M. Henry Linder a senior research analyst in the Macroeconomic and Monetary Studies Function of the Federal Reserve Bank of New York.