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It’s not just for inflation: The usefulness of the median CPI in BVAR forecasting

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  • Brent Meyer
  • Saeed Zaman

Abstract

In this paper we investigate the forecasting performance of the median CPI in a variety of Bayesian VARs (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 ?Philips-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?significantly improves the forecasts of both headline and core CPI. across our wide-ranging set of 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.

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  • Brent Meyer & Saeed Zaman, 2013. "It’s not just for inflation: The usefulness of the median CPI in BVAR forecasting," Working Papers (Old Series) 1303, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1303
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    References listed on IDEAS

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    Cited by:

    1. Amy Higgins & Randal J. Verbrugge, 2015. "Tracking Trend Inflation: Nonseasonally Adjusted Variants of the Median and Trimmed-Mean CPI," Working Papers (Old Series) 1527, Federal Reserve Bank of Cleveland.
    2. Michal Andrle & Jan Bruha & Serhat Solmaz, 2016. "On the Sources of Business Cycles: Implications for DSGE Models," Working Papers 2016/03, Czech National Bank.
    3. Anastasios Evgenidis & Anastasios G. Malliaris, 2020. "To Lean Or Not To Lean Against An Asset Price Bubble? Empirical Evidence," Economic Inquiry, Western Economic Association International, vol. 58(4), pages 1958-1976, October.
    4. Stefan Bruder, 2014. "Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions," ECON - Working Papers 181, Department of Economics - University of Zurich, revised Dec 2015.
    5. Dalibor Stevanovic & Rachidi Kotchoni, 2016. "Forecasting U.S. Recessions and Economic Activity," CIRANO Working Papers 2016s-36, CIRANO.
    6. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.
    7. Michal Andrle & Jan Bruha & Mr. Serhat Solmaz, 2016. "Output and Inflation Co-movement: An Update on Business-Cycle Stylized Facts," IMF Working Papers 2016/241, International Monetary Fund.

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    Keywords

    Bayesian statistical decision theory; Forecasting; Monetary policy; Simulation modeling;
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