Bayesian Forecasting of Federal Funds Target Rate Decisions
Sjoerd van den Hauwe,
Dick van Dijk () and
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Sjoerd van den Hauwe: Erasmus University Rotterdam
No 11-093/4, Tinbergen Institute Discussion Papers from Tinbergen Institute
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)
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Journal Article: Bayesian forecasting of federal funds target rate decisions (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20110093
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