Trimmed-Mean Inflation Statistics: Just Hit the One in the Middle

Abstract

This paper reinvestigates the performance of trimmed-mean infl ation measures some 20 years since their inception, asking whether there is a particular trimmedmean measure that dominates the median CPI. Unlike previous research, we evaluate the performance of symmetric and asymmetric trimmed-means using a well-known equality of prediction test. We fi nd that there is a large swath of trimmed-means that have statistically indistinguishable performance. Also, while the swath of statistically similar trims changes slightly over different sample periods, it always includes the median CPI-an extreme trim that holds conceptual and computational advantages. We conclude with a simple forecasting exercise that highlights the advantage of the median CPI (and trimmed-mean estimators in general) relative to other standard measures in forecasting headline infl ation.

The usefulness of the median CPI in Bayesian VARs used for macroeconomic forecasting and policy

“…We ran a few forecasting tests using the 16% trimmed-mean CPI and, consistent withMeyer and Venkatu (2012) found qualitatively similar results.5 In light of recent research (e.g Gamber et al (2015)…”

“…As to which trimmed-mean inflation statistic we should use in this exercise, we lean on the work of Meyer and Venkatu (2012), which shows that there is a wide-swath of statistically-indistinguishably performing trims. They, then, suggest the use of the median CPI because of its advantages in communicating underlying inflation to the public.3 F 4…”

It's Not Just for Inflation: The Usefulness of the Median CPI in BVAR Forecasting

“…We ran a few forecasting tests using the 16% trimmed-mean CPI and, consistent withMeyer and Venkatu (2012) found qualitatively similar results.…”

“…As to which trimmed-mean inflation statistic we should use in this exercise, we lean on the work of Meyer and Venkatu (2012), which shows that there is a wide-swath of statistically-indistinguishably performing trims. They, then, suggest the use of the median CPI because of its advantages in communicating underlying inflation to the public.…”

Forecasting GDP Growth with NIPA Aggregates

“…Because some of our aggregates omit relatively volatile components or subcomponents from GDP, our approach has parallels to parts of the inflation forecasting literature that have examined the ability of core inflation measures-which can be interpreted broadly to be inflation measures that exclude food and energy prices, focus on price changes in the center of the monthly distribution by using medians or trimmed means, or omit other components than food and energy-to predict headline inflation. The results from that literature have been mixed, with some studies reporting that various core measures produce relatively more accurate forecasts for headline inflation than does headline inflation itself, while others find little forecasting benefit from core measures vis-à-vis headline inflation; see Bryan and Cecchetti (1994), Smith (2004), Meyer and Pasaogullari (2010), Crone et al 2013, and Meyer and Venkatu (2014). The ability of core inflation to forecast headline inflation can be related to a broader literature on the usefulness of disaggregates in forecasting an aggregate; see, e.g., Lütkepohl (2006) Overall, our results suggest that NIPA aggregates that exclude inventories and trade data-which includes the measures DFP and PDFP, as well as PCE itself-are useful predictors for future GDP growth and can outforecast a typical univariate benchmark.…”