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Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?

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  • Manzan, Sebastiano
  • Zerom, Dawit

Abstract

Much of the US inflation forecasting literature deals with examining the ability of macroeconomic indicators to predict the mean of future inflation, and the overwhelming evidence suggests that the macroeconomic indicators provide little or no predictability. In this paper, we expand the scope of inflation predictability and explore whether macroeconomic indicators are useful in predicting the distribution of future inflation. To incorporate macroeconomic indicators into the prediction of the conditional distribution of future inflation, we introduce a semi-parametric approach using conditional quantiles. The approach offers more flexibility in capturing the possible role of macroeconomic indicators in predicting the different parts of the future inflation distribution. Using monthly data on US inflation, we find that unemployment rate, housing starts, and the term spread provide significant out-of-sample predictability for the distribution of core inflation. Importantly, this result is obtained for a forecast evaluation period that we intentionally chose to be after 1984, when current research shows that macroeconomic indicators do not add much to the predictability of the future mean inflation. This paper discusses various findings using forecast intervals and forecast densities, and highlights the unique insights that the distribution approach offers, which otherwise would be ignored if we relied only on mean forecasts.

Suggested Citation

  • Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:14387
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    File URL: https://mpra.ub.uni-muenchen.de/14387/1/MPRA_paper_14387.pdf
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    References listed on IDEAS

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    8. Lutz Kilian & Simone Manganelli, 2008. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1103-1129, September.
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    Citations

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    Cited by:

    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, Elsevier.
    2. Rossi, Barbara & Sekhposyan, Tatevik, 2014. "Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set," International Journal of Forecasting, Elsevier, vol. 30(3), pages 662-682.
    3. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    4. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, Elsevier.

    More about this item

    Keywords

    Conditional quantiles; Distribution; Inflation; Predictability; Phillips curve; Combining;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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