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Testing the predictive power of the term structure without data snooping bias

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  • Kao, Yi-Cheng
  • Kuan, Chung-Ming
  • Chen, Shikuan

Abstract

It is well documented that the term structure of interest rates has predictive power for real economic growth. Applying the stepwise superior predictive ability test, we find that superior models contain both a short-term rate and a term spread.

Suggested Citation

  • Kao, Yi-Cheng & Kuan, Chung-Ming & Chen, Shikuan, 2013. "Testing the predictive power of the term structure without data snooping bias," Economics Letters, Elsevier, vol. 121(3), pages 546-549.
    • Handle: RePEc:eee:ecolet:v:121:y:2013:i:3:p:546-549
    DOI: 10.1016/j.econlet.2013.10.020
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    References listed on IDEAS

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    1. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
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    Cited by:

    1. Gebka, Bartosz & Wohar, Mark E., 2018. "The predictive power of the yield spread for future economic expansions: Evidence from a new approach," Economic Modelling, Elsevier, vol. 75(C), pages 181-195.
    2. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.

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    More about this item

    Keywords

    Data snooping; GDP growth; Stepwise SPA test; Term spread;
    All these keywords.

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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