Decomposing Federal Funds Rate forecast uncertainty using real-time data

Mandler, Martin (2007): Decomposing Federal Funds Rate forecast uncertainty using real-time data.

Abstract

This paper uses real-time data for the U.S. to estimate out-of-sample forecast uncertainty about the Federal Funds Rate. By combining a Taylor rule with an unobserved components model of economic fundamentals I separate forecast uncertainty into economically interpretable components that represent uncertainty about future economic conditions and uncertainty about future monetary policy. The estimation results indicate important time variation in uncertainty about the future Federal Funds Rate.

• Item Type: MPRA Paper
• Institution: University of Giessen
• Language: English
• Keywords: monetary policy reaction function, interest rate uncertainty, state-space model
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy

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