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Improving Forecasts of Inflation using the Term Structure of Interest Rates

Author

Listed:
  • Alonso Gomez
  • John M Maheu
  • Alex Maynard

Abstract

Many pricing models imply that nominal interest rates contain information on inflation expectations. This has lead to a large empirical literature that investigates the use of interest rates as predictors of future inflation. Most of these focus on the Fisher hypothesis in which the interest rate maturity matches the inflation horizon. In general forecast improvements have been modest and often fail to improve on autoregressive benchmarks. Rather than use only monthly interest rates that match the maturity of inflation, this paper advocates using the whole term structure of daily interest rates and their lagged values to forecast monthly inflation. Principle component methods are employed to combine information from interest rates across both the term structure and time series dimensions. We find robust forecasting improvements in general as compared to both an augmented Fisher equation and autoregressive benchmarks.

Suggested Citation

  • Alonso Gomez & John M Maheu & Alex Maynard, 2008. "Improving Forecasts of Inflation using the Term Structure of Interest Rates," Working Papers tecipa-319, University of Toronto, Department of Economics.
  • Handle: RePEc:tor:tecipa:tecipa-319
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    References listed on IDEAS

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    More about this item

    Keywords

    inflation; inflation forecast; Fisher equation; term structure; principal components;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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