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Measuring Short-Run Inflation for Central Bankers

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  • Stephen G. Cecchetti

Abstract

As central bankers intensify their focus on inflation as the primary goal of monetary policy, it becomes increasingly important to have accurate and reliable measures of changes in the aggregate price level. Measuring inflation is surprisingly difficult, involving two types of problems. Commonly used indices, such as the Consumer Price Index (CPI), contain both transitory noise and bias. Noise causes short-run changes in measured inflation to inaccurately reflect movements in long-run trends, while bias leads the long-run average change in the CPI to be too high. In this paper I propose methods of reducing both the noise and the bias in the CPI. Noise reduction is achieved by average monthly inflation in measures called trimmed means' over longer horizons. Trimmed means are statistics similar to the median that are calculated by ignoring the CPI components with extreme high and low changes each month, and averaging the rest. I find that using three month averages halves the noise, while removing the highest and lowest ten percent of the cross-sectional distribution of inflation reduces the monthly variation in inflation by one-fifth.

Suggested Citation

  • Stephen G. Cecchetti, 1996. "Measuring Short-Run Inflation for Central Bankers," NBER Working Papers 5786, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:5786
    Note: ME
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    References listed on IDEAS

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    1. Wynne, Mark A & Sigalla, Fiona D, 1996. "A Survey of Measurement Biases in Price Indexes," Journal of Economic Surveys, Wiley Blackwell, vol. 10(1), pages 55-89, March.
    2. Michael F. Bryan & Stephen G. Cecchetti, 1993. "The consumer price index as a measure of inflation," Economic Review, Federal Reserve Bank of Cleveland, vol. 29(Q IV), pages 15-24.
    3. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    4. Stephen G. Cecchetti & Anil K. Kashyap & David W. Wilcox, 1995. "Do Firms Smooth the Seasonal in Production in a Boom? Theory and Evidence," NBER Working Papers 5011, National Bureau of Economic Research, Inc.
    5. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    6. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    7. Laurence Ball & N. Gregory Mankiw, 1995. "Relative-Price Changes as Aggregate Supply Shocks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 161-193.
    8. Gordon, Robert J, 1992. "Measuring the Aggregate Price Level: Implications for Economic Performance and Policy," CEPR Discussion Papers 663, C.E.P.R. Discussion Papers.
    9. David E. Lebow & John M. Roberts & David J. Stockton, 1992. "Economic performance under price stability," Working Paper Series / Economic Activity Section 125, Board of Governors of the Federal Reserve System (U.S.).
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    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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