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Does the ROMC have expertise, and can it forecast?

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Author Info
Franses, Ph.H.B.F.
McAleer, M.J.
Legerstee, R. (Erasmus Econometric Institute)

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Abstract

The primary purpose of the paper is to answer the following two questions regarding the performance of the influential Federal Open Market Committee (FOMC) of the Federal Reserve System, in comparison with the forecasts contained in the “Greenbooks†of the professional staff of the Board of Governors: Does the FOMC have expertise, and can it forecast better than the staff? The FOMC forecasts that are analyzed in practice are non-replicable forecasts. In order to evaluate such forecasts, this paper develops a model to generate replicable FOMC forecasts, and compares the staff forecasts, non-replicable FOMC forecasts, and replicable FOMC forecasts, considers optimal forecasts and efficient estimation methods, and presents a direct test of FOMC expertise on non-replicable FOMC forecasts. The empirical analysis of Romer and Romer (2008) is re-examined to evaluate whether their criticisms of the FOMC’s forecasting performance should be accepted unreservedly, or might be open to alternative interpretations.

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Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2008-33 Revision_Date: 2009-11-26.

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Date of creation: 01 Dec 2008
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Handle: RePEc:dgr:eureir:1765013980

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Related research
Keywords: direct test of FOMC expertise; FOMC forecast; forecast performance; replicable FOMC; staff forecast;

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  1. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September. [Downloadable!]
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  2. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May. [Downloadable!] (restricted)
  3. McAleer, Michael, 1992. "Efficient Estimation: The Rao-Zyskind Condition, Kruskal's Theorem and Ordinary Least Squares," The Economic Record, The Economic Society of Australia, vol. 68(200), pages 65-72, March.
  4. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009. "Expert opinion versus expertise in forecasting," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346. [Downloadable!] (restricted)
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  5. Fiebig, Denzil G. & McAleer, Michael & Bartels, Robert, 1992. "Properties of ordinary least squares estimators in regression models with nonspherical disturbances," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 321-334. [Downloadable!] (restricted)
  6. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December. [Downloadable!] (restricted)
  7. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October. [Downloadable!] (restricted)
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