Bayesian Forecasting of Federal Funds Target Rate Decisions
2011-07-13
Research Paper
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This paper examines which macroeconomic and financial variables are most informative for the federal funds target rate decisions made by the Federal Open Market Committee (FOMC) from a forecasting perspective. The analysis is conducted for the FOMC decision during the period January 1990 - June 2008, using dynamic ordered probit models with a Bayesian endogenous variable selection methodology and real-time data for a set of 33 candidate predictor variables. We find that indicators of economic activity and forward-looking term structure variables as well as survey measures have most predictive ability. For the full sample period, in-sample probability forecasts achieve a hitrate of 90 percent. Based on out-of-sample forecasts for the period January 2001 - June 2008, 82 percent of the FOMC decisions are predicted correctly.
- importance sampling
- Bayesian analysis
- variable selection
- Federal funds target rate
- dynamic ordered probit
- real-time forecasting
- C25 : Discrete Regression and Qualitative Choice Models; Discrete Regressors
- E52 : Monetary Policy (Targets, Instruments, and Effects)
- E58 : Central Banks and Their Policies
- C53 : Forecasting and Other Model Applications
- C11 : Bayesian Analysis
- target
- model
- decision
- variable
- probability
- target rate decisions
- probit model
- target rate
- period
- probit
- predictor
- forecast
- month
- e ffect
- sample
- meeting
- forecasting
- result
- distribution
- parameter
