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.

Additional Metadata
Keywords Bayesian analysis, Federal funds target rate, dynamic ordered probit, importance sampling, real-time forecasting, variable selection
Publisher Tinbergen Institute
Persistent URL hdl.handle.net/1765/25708
Note Tinbergen Institute Discussion Papers No. 2011-093/4
Citation
van den Hauwe, S., van Dijk, D.J.C., & Paap, R.. (2011). Bayesian Forecasting of Federal Funds Target Rate Decisions (No. TI 11-093/4). Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/25708