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Signal or noise? Implications of the term premium for recession forecasting

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  • Samuel Maurer
  • Joshua V. Rosenberg

Abstract

Since the 1970s, an inverted yield curve has been a reliable signal of an imminent recession. One interpretation of this signal is that markets expect monetary policy to ease as the Federal Reserve responds to an upcoming deterioration in economic conditions. Some have argued that the yield curve inversion in August 2006 did not signal an imminent recession, but instead was triggered by an unusually low level of the term premium. This article examines whether changes in the term premium can distort the recession signal given by an inverted yield curve. The authors use the Kim and Wright (2005) decomposition of the term spread into an expectations component and a term premium component to compare recession forecasting models with and without the term premium. They find that the expectations component of the term spread is a leading indicator of recession, while the term premium component is not. Their analysis of recession forecasting performance provides some evidence that a model based on the expectations component is more accurate than the standard model that uses the term spread.

Suggested Citation

  • Samuel Maurer & Joshua V. Rosenberg, 2008. "Signal or noise? Implications of the term premium for recession forecasting," Economic Policy Review, Federal Reserve Bank of New York, vol. 14(Jul), pages 1-11.
  • Handle: RePEc:fip:fednep:y:2008:i:jul:p:1-11:n:v.14no.1
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    Cited by:

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    3. Jessica James & Michael Leister & Christoph Rieger, 2017. "An empirical method of calculating the term premium," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1783-1793, December.
    4. Aguilar-Argaez Ana María & Diego-Fernández Forseck María & Elizondo Rocío & Roldán-Peña Jessica, 2020. "Term Premium Dynamics and its Determinants: The Mexican Case," Working Papers 2020-18, Banco de México.
    5. Lange, Ronald Henry, 2018. "The predictive content of the term premium for GDP growth in Canada: Evidence from linear, Markov-switching and probit estimations," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 80-91.
    6. Firdous Ahmad Shah & Lokenath Debnath, 2017. "Wavelet Neural Network Model for Yield Spread Forecasting," Mathematics, MDPI, vol. 5(4), pages 1-15, November.
    7. Evgenidis, Anastasios & Papadamou, Stephanos & Siriopoulos, Costas, 2020. "The yield spread's ability to forecast economic activity: What have we learned after 30 years of studies?," Journal of Business Research, Elsevier, vol. 106(C), pages 221-232.
    8. Morell, Joseph, 2018. "The decline in the predictive power of the US term spread: A structural interpretation," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 314-331.
    9. Jardet, Caroline & Monfort, Alain & Pegoraro, Fulvio, 2013. "No-arbitrage Near-Cointegrated VAR(p) term structure models, term premia and GDP growth," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 389-402.
    10. Kuang-Liang Chang & Nan-Kuang Chen & Charles Ka Yui Leung, 2016. "Losing Track of the Asset Markets: the Case of Housing and Stock," International Real Estate Review, Global Social Science Institute, vol. 19(4), pages 435-492.
    11. Zakamulin, Valeriy, 2013. "Forecasting the size premium over different time horizons," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1061-1072.

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