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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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Bibliographic Info

Paper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1303.

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Date of creation: 2013
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Handle: RePEc:fip:fedcwp:1303

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

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References

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  1. 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.
  2. Banbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
  3. 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, 03.
  4. Jim Dolmas, 2005. "Trimmed mean PCE inflation," Working Papers 0506, Federal Reserve Bank of Dallas.
  5. Smith, Julie K, 2004. "Weighted Median Inflation: Is This Core Inflation?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(2), pages 253-63, April.
  6. 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 254-59, April.
  7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  8. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper 96-13, Federal Reserve Bank of Atlanta.
  9. 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.
  10. 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.
  11. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
  12. Michael F. Bryan & Stephen G. Cecchetti, 1994. "Measuring Core Inflation," NBER Chapters, in: Monetary Policy, pages 195-219 National Bureau of Economic Research, Inc.
  13. Kenneth Beauchemin & Saeed Zaman, 2011. "A medium scale forecasting model for monetary policy," Working Paper 1128, Federal Reserve Bank of Cleveland.
  14. Michael F. Bryan & Stephen G. Cecchetti & Rodney L. Wiggins II, 1997. "Efficient Inflation Estimation," NBER Working Papers 6183, National Bureau of Economic Research, Inc.
  15. Andrea Carriero & Todd Clark & Massimiliano Marcellino, 2011. "Bayesian VARs: specification choices and forecast accuracy," Working Paper 1112, Federal Reserve Bank of Cleveland.
  16. Todd Clark & Michael W. McCracken, 2011. "Tests of equal forecast accuracy for overlapping models," Working Paper 1121, Federal Reserve Bank of Cleveland.
  17. Brent Meyer & Guhan Venkatu, 2012. "Trimmed-mean inflation statistics: just hit the one in the middle," Working Paper 1217, Federal Reserve Bank of Cleveland.
  18. Brent Meyer & Mehmet Pasaogullari, 2010. "Simple ways to forecast inflation: what works best?," Economic Commentary, Federal Reserve Bank of Cleveland, issue Dec.
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