Bayesian Forecasting of Federal Funds Target Rate Decisions
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.
|Keywords||Bayesian analysis, Federal funds target rate, dynamic ordered probit, importance sampling, real-time forecasting, variable selection|
|JEL||C11, Bayesian Analysis (jel), C25, Discrete Regression and Qualitative Choice Models; Discrete Regressors (jel), C53, Forecasting and Other Model Applications (jel), E52, Monetary Policy (Targets, Instruments, and Effects) (jel), E58, Central Banks and Their Policies (jel)|
|Note||Tinbergen Institute Discussion Papers No. 2011-093/4|
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 Discussion Paper Series. Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/25708