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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.

Suggested Citation

  • 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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    1. Brent Meyer & Guhan Venkatu, 2012. "Trimmed-mean inflation statistics: just hit the one in the middle," Working Papers (Old Series) 1217, Federal Reserve Bank of Cleveland.
    2. Laurence Ball & Sandeep Mazumder, 2011. "Inflation Dynamics and the Great Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 42(1 (Spring), pages 337-405.
    3. Kenneth Beauchemin & Saeed Zaman, 2011. "A medium scale forecasting model for monetary policy," Working Papers (Old Series) 1128, Federal Reserve Bank of Cleveland.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    5. Michael F. Bryan & Stephen G. Cecchetti, 1994. "Measuring Core Inflation," NBER Chapters, in: Monetary Policy, pages 195-219, National Bureau of Economic Research, Inc.
    6. Todd E. Clark & Michael W. Mccracken, 2014. "Tests Of Equal Forecast Accuracy For Overlapping Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 415-430, April.
    7. David Norman & Anthony Richards, 2012. "The Forecasting Performance of Single Equation Models of Inflation," The Economic Record, The Economic Society of Australia, vol. 88(280), pages 64-78, March.
    8. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    9. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    10. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    11. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    12. Alan K. Detmeister, 2011. "The usefulness of core PCE inflation measures," Finance and Economics Discussion Series 2011-56, Board of Governors of the Federal Reserve System (U.S.).
    13. Theodore M. Crone & N. Neil K. Khettry & Loretta J. Mester & Jason A. Novak, 2013. "Core Measures of Inflation as Predictors of Total Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(2‐3), pages 505-519, March.
    14. Jim Dolmas, 2005. "Trimmed mean PCE inflation," Working Papers 0506, Federal Reserve Bank of Dallas.
    15. Andrea Brischetto & Anthony Richards, 2006. "The Performance of Trimmed Mean Measures of Underlying Inflation," RBA Research Discussion Papers rdp2006-10, Reserve Bank of Australia.
    16. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    17. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    18. Cogley, Timothy, 2002. "A Simple Adaptive Measure of Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 94-113, February.
    19. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    20. Michael F. Bryan & Stephen G. Cecchetti & Rodney L. Wiggins II, 1997. "Efficient Inflation Estimation," NBER Working Papers 6183, National Bureau of Economic Research, Inc.
    21. Brent Meyer & Mehmet Pasaogullari, 2010. "Simple ways to forecast inflation: what works best?," Economic Commentary, Federal Reserve Bank of Cleveland, issue Dec.
    22. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    23. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    24. Smith, Julie K, 2004. "Weighted Median Inflation: Is This Core Inflation?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(2), pages 253-263, April.
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    Citations

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    Cited by:

    1. Michal Andrle & Jan Bruha & Serhat Solmaz, 2016. "On the Sources of Business Cycles: Implications for DSGE Models," Working Papers 2016/03, Czech National Bank.
    2. 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.
    3. Dalibor Stevanovic & Rachidi Kotchoni, 2016. "Forecasting U.S. Recessions and Economic Activity," CIRANO Working Papers 2016s-36, CIRANO.
    4. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.
    5. 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.
    6. 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.
    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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