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Federal funds rate prediction

Author

Listed:
  • Lucio Sarno
  • Daniel L. Thornton
  • Giorgio Valente

Abstract

We examine the forecasting performance of a range of time-series models of the daily US effective federal funds (FF) rate recently proposed in the literature. We find that: (i) most of the models and predictor variables considered produce satisfactory one-day-ahead forecasts of the FF rate; (ii) the best forecasting model is a simple univariate model where the future FF rate is forecast using the current difference between the FF rate and its target; (iii) combining the forecasts from various models generally yields modest improvements on the best performing model. These results have a natural interpretation and clear policy implications.

Suggested Citation

  • Lucio Sarno & Daniel L. Thornton & Giorgio Valente, 2004. "Federal funds rate prediction," Working Papers 2002-005, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2002-005
    DOI: 10.20955/wp.2002.005
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    Cited by:

    1. Artem Meshcheryakov & Stoyu I Ivanov, 2017. "Investor's sentiment in predicting the Effective Federal Funds Rate," Economics Bulletin, AccessEcon, vol. 37(4), pages 2767-2796.
    2. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2025. "Persistence and Nonlinearities in the US Federal Funds Rate," CESifo Working Paper Series 11913, CESifo.
    3. Katsuhiro Sugita, 2017. "Time Series Analysis of the US Term Structure of Interest Rates Using a Bayesian Markov Switching Cointegration Model," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(3), pages 49-56, March.
    4. Kirstin Hubrich & Kenneth D. West, 2010. "Forecast evaluation of small nested model sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
    5. Tillmann, Peter, 2007. "Inflation regimes in the US term structure of interest rates," Economic Modelling, Elsevier, vol. 24(2), pages 203-223, March.
    6. Thomadakis, Apostolos, 2016. "Do Combination Forecasts Outperform the Historical Average? Economic and Statistical Evidence," MPRA Paper 71589, University Library of Munich, Germany.
    7. Todd E. Clark & Michael W. McCracken, 2010. "Reality checks and nested forecast model comparisons," Working Papers 2010-032, Federal Reserve Bank of St. Louis.
    8. Feunou Bruno & Fontaine Jean-Sébastien & Jin Jianjian, 2021. "What model for the target rate," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-23, February.
    9. Sarno, Lucio & Thornton, Daniel L., 2003. "The dynamic relationship between the federal funds rate and the Treasury bill rate: An empirical investigation," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1079-1110, June.
    10. Bjørn-Roger Wilhelmsen & Andrea Zaghini, 2011. "Monetary policy predictability in the euro area: an international comparison," Applied Economics, Taylor & Francis Journals, vol. 43(20), pages 2533-2544.
    11. Friedrich Heinemann & Katrin Ullrich, 2007. "Does it Pay to Watch Central Bankers’ Lips? The Information Content of ECB Wording," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 143(II), pages 155-185, June.
    12. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2017. "Persistence and cycles in the us federal funds rate," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 1-8.
    13. Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, vol. 150(2), pages 297-311, June.
    14. James D. Hamilton, 2008. "Assessing monetary policy effects using daily federal funds futures contracts," Review, Federal Reserve Bank of St. Louis, vol. 90(Jul), pages 377-394.
    15. Ramos-Tallada, Julio, 2015. "Bank risks, monetary shocks and the credit channel in Brazil: Identification and evidence from panel data," Journal of International Money and Finance, Elsevier, vol. 55(C), pages 135-161.
    16. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    17. William R. Emmons & Aeimit K. Lakdawala & Christopher J. Neely, 2006. "What are the odds? option-based forecasts of FOMC target changes," Review, Federal Reserve Bank of St. Louis, vol. 88(Nov), pages 543-562.
    18. Tillmann, Peter, 2003. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Bonn Econ Discussion Papers 27/2003, University of Bonn, Bonn Graduate School of Economics (BGSE).
    19. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2016. "Can commodity returns forecast Canadian sector stock returns?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 172-188.
    20. PeterTillmann, 2004. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Computing in Economics and Finance 2004 53, Society for Computational Economics.
    21. Marco Lippi & Daniel L. Thornton, 2004. "A Dynamic Factor Analysis of the Response of U.S. Interest Rates to News," LEM Papers Series 2004/05, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    22. James D. Hamilton, 2007. "Assessing Monetary Policy Effects Using Daily Fed Funds Futures Contracts," NBER Working Papers 13569, National Bureau of Economic Research, Inc.
    23. Kim, Hyerim & Kang, Kyu Ho, 2022. "The Bank of Korea watch," Journal of International Money and Finance, Elsevier, vol. 126(C).
    24. Kosei Fukuda, 2011. "Cointegration rank switching model: an application to forecasting interest rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 509-522, August.

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    Keywords

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    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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