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Improving Forecasts of Inflation using the Term Structure of Interest Rates Author info | Abstract | Publisher info | Download info | Related research | Statistics Alonso Gomez
John M Maheu
Alex Maynard
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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.
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Paper provided by University of Toronto, Department of Economics in its series Working Papers with number
tecipa-319.
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Length: 34 pages
Date of creation: 16 May 2008Date of revision:
Handle: RePEc:tor:tecipa:tecipa-319Contact details of provider: Postal: 150 St. George Street, Toronto, Ontario Phone: (416) 978-5283 Fax: (416) 978-6713
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Keywords: inflation ; inflation forecast ; Fisher equation ; term structure ; principal components ; Other versions of this item:
Find related papers by 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 C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
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