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Bayesian Forecasting of Federal Funds Target Rate Decisions

Sjoerd van den Hauwe, Dick van Dijk () and Richard Paap
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Sjoerd van den Hauwe: Erasmus University Rotterdam

No 11-093/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: 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. This discussion paper resulted in an article in the Journal of Macroeconomics (2013). Volume 37, pages 19-40.

Keywords: Federal funds target rate; real-time forecasting; dynamic ordered probit; variable selection; Bayesian analysis; importance sampling (search for similar items in EconPapers)
JEL-codes: E52 E58 C25 C11 C53 (search for similar items in EconPapers)
Date: 2011-07-15
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