Core Inflation and Trend Inflation June 2015

An important input to monetary policymaking is estimating the current level of inflation. This paper examines empirically whether the measurement of trend inflation can be improved by using disaggregated data on sectoral inflation to construct indexes akin to core inflation but with a time-varying distributed lags of weights, where the sectoral weight depends on the timevarying volatility and persistence of the sectoral inflation series and on the comovement among sectors. The model is estimated using U.S. data on 17 components of the personal consumption expenditure inflation index. The modeling framework is a dynamic factor model with timevarying coefficients and stochastic volatility as in del Negro and Otrok (2008); this is the multivariate extension of the univariate unobserved components-stochastic volatility model of trend inflation in Stock and Watson (2007). Our main empirical results are (i) the resulting multivariate estimate of trend inflation is similar to the univariate estimate of trend inflation computed using core PCE inflation (excluding food and energy) in the first half of the sample, but introduces food in the second half of the sample; (ii) the model-based uncertainty about trend headline inflation is substantially reduced by using the disaggregated series in a multivariate model; (iii) the multivariate and univariate trends constructed using core measures of inflation forecast average inflation over the 1-3 year horizon more accurately than a variety of other benchmark inflation measures, although there is considerable sampling uncertainty in these forecast comparisons.