Discussions of the goals of monetary policy generally focus on the benefits of price and output stabilization. After formulating a loss function that weights these two objectives, the next step is to examine different policy programs and operating procedures in order to achieve the desired outcomes. But these discussions take for granted our ability to measure the objects of interest, namely, aggregate price inflation and the level of output. Unfortunately, the measurement of aggregate inflation as a monetary phenomenon is difficult, as nonmonetary events, such as sector-specific shocks and measurement errors, can temporarily produce noise in the price data that substantially affects the aggregate price indices at higher frequencies. During periods of poor weather, for example, food prices may rise to reflect decreased supply, thereby producing transitory increases in the aggregate index. Because these price changes do not constitute underlying monetary inflation, the monetary authorities should avoid basing their decisions on them. Solutions to the problem of high-frequency noise in the price data include calculating low-frequency trends over which this noise is reduced. But from a policymaker's perspective, this greatly reduces the timeliness, and therefore

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This paper investigates the use of trimmed means as high-frequency estimators of inflation. The known characteristics of price change distributions, specifically the observation that they generally exhibit high levels of kurtosis, imply that simple averages of price data are unlikely to produce efficient estimates of inflation. Trimmed means produce superior estimates of 'core inflation,' which we define as a long-run centered moving average of CPI and PPI inflation, We find that trimming 9% from each tail of the CPI price-change distribution, or 45% from the tails of the PPI price-change distribution, yields an efficient estimator of core inflation for these two series, although lesser trims also produce substantial efficiency gains. Historically, the optimal trimmed estimators are found to be nearly 23% more efficient (in terms of root-mean-square error) than the standard mean CPI, and 45% more efficient than the mean PPI. Moreover, the efficient estimators are robust to sample period and to the definition of the presumed underlying long-run trend in inflation.

Price movements are the channel through which market information is transmitted. An increase in one price relative to others is the signal that directs resources and rations consumption. In other words, markets operate through the distribution of prices.

This paper investigates the use of trimmed means as high-frequency estimators of inflation. The known characteristics of price change distributions, specifically the . observation that they generally exhibit high levels of kurtosis, imply that simple averages of price data are unlikely to produce efficient estimates of inflation. Trimmed means produce superior estimates of 'core inflation,' which we define as a long-run centered moving average of CPI and PPI inflation. We find that trimming 9% from each tail of the CPI price-change distribution, or 45% from the tails of the PPI price-change distribution, yields an efficient estimator of core inflation for these two series, although lesser trims also produce substantial efficiency gains. Historically, the optimal trimmed estimators are found to be nearly 23% more efficient (in terms of root-mean-square error) than the standard mean CPI, and 45% more efficient than the mean PPI. Moreover, the efficient estimators are robust to sample period and to the definition of the presumed underlying long-run trend in inflation.