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Interest rate forecasts: a pathology

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  • Goodhart, Charles
  • Bin Lim, Wen

Abstract

This is the first of three prospective papers examining how well forecasters can predict the future time path of short-term interest rates. Most prior work has been done using US data; in this exercise we use forecasts made for New Zealand (NZ) by the Reserve Bank of New Zealand (RBNZ), and those derived from money market yield curves in the UK. In this first exercise we broadly replicate recent US findings for NZ and UK, to show that such forecasts in NZ and UK have been excellent for the immediate forthcoming quarter, reasonable for the next quarter and useless thereafter. Moreover, when ex post errors are assessed depending on whether interest rates have been upwards, or downwards, trending, they are shown to have been biased and, apparently, inefficient. In the second paper we shall examine whether (NZ and UK) forecasts for inflation exhibit the same syndromes, and whether errors in inflation forecasts can help to explain errors in interest rate forecasts. In the third paper we shall set out an hypothesis to explain those findings, and examine whether the apparent ex post forecast inefficiencies may still be consistent with ex ante forecast efficiency. Even if the forecasts may be ex ante efficient, their negligible ex post forecasting ability suggests that, beyond a six months’ horizon from the forecast date, they would be better replaced by a simple ‘no-change thereafter’ assumption.

Suggested Citation

  • Goodhart, Charles & Bin Lim, Wen, 2008. "Interest rate forecasts: a pathology," LSE Research Online Documents on Economics 24431, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:24431
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    File URL: http://eprints.lse.ac.uk/24431/
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    References listed on IDEAS

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    1. Malin Adolfson & Michael K. Andersson & Jesper Lindé & Mattias Villani & Anders Vredin, 2007. "Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 111-144, December.
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    4. Rudebusch, Glenn D., 2002. "Term structure evidence on interest rate smoothing and monetary policy inertia," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1161-1187, September.
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    6. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
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    8. Glenn D. Rudebusch, 2007. "Monetary policy inertia and recent Fed actions," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue jan26.
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    Cited by:

    1. Thornton, Daniel L., 2014. "Monetary policy: Why money matters (and interest rates don’t)," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 202-213.
    2. Daniel L. Thornton, 2009. "How did we get to inflation targeting and where do we go now? a perspective from the U.S. experience," Working Papers 2009-038, Federal Reserve Bank of St. Louis.
    3. Guidolin, Massimo & Thornton, Daniel L., 2018. "Predictions of short-term rates and the expectations hypothesis," International Journal of Forecasting, Elsevier, vol. 34(4), pages 636-664.
    4. Daniel L. Thornton, 2018. "Greenspan's Conundrum and the Fed's Ability to Affect Long‐Term Yields," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 513-543, March.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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