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The role of commodity prices in forecasting U.S. core inflation

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Abstract

This note documents a curious finding about the substantial forecast ability of a simple aggregator of three commodity futures prices for U.S. core inflation. The proposed aggregator reduces the out-of-sample root mean squared error for 12-month-ahead inflation forecasts of the benchmark AR(1) model by 28 percent (20 percent) for the PCE (CPI) measure of core inflation. To avoid obfuscation of the sources of forecast ability, the model is intentionally kept simple, although extensions for improving and increasing the robustness of the forecast procedure are also discussed.

Suggested Citation

  • Nikolay Gospodinov, 2016. "The role of commodity prices in forecasting U.S. core inflation," FRB Atlanta Working Paper 2016-5, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:2016-05
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    References listed on IDEAS

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    1. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, January.
    2. Nikolay Gospodinov & Bin Wei, 2016. "Forecasts of inflation and interest rates in no-arbitrage affine models," FRB Atlanta Working Paper 2016-3, Federal Reserve Bank of Atlanta.
    3. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    4. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    5. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    6. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
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    Cited by:

    1. Anthony Garratt & Ivan Petrella, 2022. "Commodity prices and inflation risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 392-414, March.
    2. Gospodinov, Nikolay & Maasoumi, Esfandiar, 2021. "Generalized aggregation of misspecified models: With an application to asset pricing," Journal of Econometrics, Elsevier, vol. 222(1), pages 451-467.

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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