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Using the Yield Curve in Forecasting Output Growth and In?flation

Author

Listed:
  • Eric Hillebrand

    (Aarhus University and CREATES)

  • Huiyu Huang

    (GMO Emerging Markets)

  • Tae-Hwy Lee

    (University of California, Riverside)

  • Canlin Li

    (Federal Reserve Board)

Abstract
Following Diebold and Li (2006), we use the Nelson-Siegel (NS, 1987) yield curve factors. However the NS yield curve factors are not supervised for a specifi?c forecast target in the sense that the same factors are used for forecasting different variables, e.g., output growth or infl?ation. We propose a modifed NS factor model, where the new NS yield curve factors are supervised for a specifi?c variable to forecast. We show it outperforms the conventional (non-supervised) NS factor model in out-of-sample forecasting of monthly US output growth and infl?ation. The original NS yield factor model is to combine information (CI) of predictors and uses factors of predictors (yield curve). The new supervised NS factor model is to combine forecasts (CF) and uses factors of forecasts of output growth or infl?ation conditional on the yield curve. We formalize the concept of supervision, and demonstrate analytically and numerically how supervision works. For both CF and CI schemes, principal components (PC) may be used in place of the NS factors. In out-of-sample forecasting of U.S. monthly output growth and infl?ation, we fi?nd that supervised CF-factor models (CF-NS, CF-PC) are substantially better than unsupervised CI-factor models (CI-NS, CI-PC), especially at longer forecast horizons.

Suggested Citation

  • Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2011. "Using the Yield Curve in Forecasting Output Growth and In?flation," CREATES Research Papers 2012-17, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2012-17
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    File URL: https://repec.econ.au.dk/repec/creates/rp/12/rp12_17.pdf
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    Citations

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    Cited by:

    1. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," Advances in Econometrics, in: 30th Anniversary Edition, pages 171-196, Emerald Group Publishing Limited.
    2. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    3. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    4. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.

    More about this item

    Keywords

    Level; slope; and curvature of the yield curve; Nelson-Siegel factors; Supervised factor models; Combining forecasts; Principal components.;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • G1 - Financial Economics - - General Financial Markets

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