Improving Inflation Forecasts Using Robust Measures

Theory and extant empirical evidence suggest that the cross-sectional asymmetry across disaggregated price indexes might be useful in forecasting aggregate inflation. Trimmed-mean inflation estimators have been shown to be useful devices for forecasting headline PCE inflation. But is this because they signal the underlying trend or because they implicitly signal asymmetry in the underlying distribution? We address this question by augmenting a "hard" to beat benchmark headline PCE inflation forecasting model with robust trimmed-mean inflation measures and robust measures of the cross-sectional skewness, both computed using the 180+ components of the PCE price index. Our results indicate significant gains in the point and density accuracy of PCE inflation forecasts over medium- and longer-term horizons, up through and including the COVID-19 pandemic. Improvements in accuracy stem mainly from the trend information implicit in trimmed-mean estimators, but skewness information is also useful. An examination of goods and services PCE inflation provides similar inference.