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Bayesian Forecasting of Federal Funds Target Rate Decisions

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  • Sjoerd van den Hauwe

    (Erasmus University Rotterdam)

  • Dick van Dijk

    (Erasmus University Rotterdam)

  • Richard Paap

    (Erasmus University Rotterdam)

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.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-093/4.

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Date of creation: 15 Jul 2011
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Handle: RePEc:dgr:uvatin:20110093

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Web page: http://www.tinbergen.nl

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Keywords: Federal funds target rate; real-time forecasting; dynamic ordered probit; variable selection; Bayesian analysis; importance sampling;

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