Bayesian Forecasting of Federal Funds Target Rate Decisions
Tinbergen Institute Discussion Paper No. 11-093/4
Posted: 15 Jul 2011
Date Written: July 13, 2011
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 hit rate 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.
Keywords: federal funds target rate, real-time forecasting, dynamic ordered probit, variable selection, Bayesian analysis, importance sampling
JEL Classification: E52, E58, C25, C11, C53
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