Bayesian forecasting of federal funds target rate decisions

• Fed funds target rate decisions are predicted using dynamic ordered probit models.
• We examine which economic and financial variables have most predictive ability.
• Bayesian model averaging is used to resolve model uncertainty.
• Indicators of economic activity and term structure variables are most informative.
• For the period 2001–2008, 82% of the FOMC decisions are predicted correctly.

In this paper we examine which macroeconomic and financial variables have most predictive ability for the federal funds target rate decisions made by the Federal Open Market Committee (FOMC). We conduct the analysis for the 157 FOMC decisions 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 are most informative from a forecasting perspective. For the full sample period, in-sample probability forecasts achieve a hit rate of 90%. Based on out-of-sample forecasts for the period January 2001–June 2008, 82% of the FOMC decisions are predicted correctly.