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Much of the US inflation forecasting literature deals with examining the ability ofmacroeconomic indicators to predict the mean of future inflation, and the overwhelmingevidence suggests that the macroeconomic indicators provide little or no predictability.In this paper, we expand the scope of inflation predictability and explorewhether macroeconomic indicators are useful in predicting the distribution of futureinflation. To incorporate macroeconomic indicators into the prediction of the conditionaldistribution of future inflation, we introduce a semi-parametric approach usingconditional quantiles. The approach offers more flexibility in capturing the possiblerole of macroeconomic indicators in predicting the different parts of the futureinflation distribution. Using monthly data on US inflation, we find that unemploymentrate, housing starts, and the term spread provide significant out-of-sample predictabilityfor the distribution of core inflation. Importantly, this result is obtainedfor a forecast evaluation period that we intentionally chose to be after 1984, whencurrent research shows that macroeconomic indicators do not add much to the predictabilityof the future mean inflation. This paper discusses various findings usingforecast intervals and forecast densities, and highlights the unique insights that thedistribution approach offers, which otherwise would be ignored if we relied only onmean forecasts.

Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?