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

Author

Listed:
  • Brent Meyer

    () (Federal Reserve Bank of Atlanta (Policy Advisor and Economist)
    Emory University)

  • Saeed Zaman

    () (Federal Reserve Bank of Cleveland (Economist)
    University of Strathclyde)

Abstract

In this paper, we investigate the forecasting performance of the median Consumer Price Index (CPI) in a variety of Bayesian Vector Autoregressions (BVARs) that are often used for monetary policy. Until now, the use of trimmed-mean price statistics in forecasting inflation has often been relegated to simple univariate or “Phillips-Curve” approaches, thus limiting their usefulness in applications that require consistent forecasts of multiple macro-variables. We find that inclusion of an extreme trimmed-mean measure—the median CPI—improves the forecasts of both core and headline inflation (CPI and personal consumption expenditures price index) across our set of monthly and quarterly BVARs. While the inflation forecasting improvements are perhaps not surprising given the current literature on core inflation statistics, we also find that inclusion of the median CPI improves the forecasting accuracy of the central bank’s primary instrument for monetary policy—the federal funds rate. We conclude with a few illustrative exercises that highlight the usefulness of using the median CPI.

Suggested Citation

  • Brent Meyer & Saeed Zaman, 2019. "The usefulness of the median CPI in Bayesian VARs used for macroeconomic forecasting and policy," Empirical Economics, Springer, vol. 57(2), pages 603-630, August.
  • Handle: RePEc:spr:empeco:v:57:y:2019:i:2:d:10.1007_s00181-018-1472-1
    DOI: 10.1007/s00181-018-1472-1
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    Cited by:

    1. Daniel R. Carroll & Randal Verbrugge, 2019. "Behavior of a New Median PCE Measure: A Tale of Tails," Economic Commentary, Federal Reserve Bank of Cleveland, issue July.

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    More about this item

    Keywords

    Inflation forecasting; Trimmed-mean estimators; Bayesian Vector Autoregression; Conditional forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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