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Using the Entire Yield Curve in Forecasting Output and Inflation

Author

Listed:
  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

  • Eric Hillebrand

    (CREATES, Aarhus University)

  • Huiyu Huang

    (ICBC Credit Suisse Asset Management)

  • 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 specific forecast target in the sense that the same factors are used for forecasting different variables, e.g., output growth or inflation. We propose a modifed NS factor model, where the new NS yield curve factors are supervised for a specific target variable to forecast. We show that it outperforms the conventional (non-supervised) NS factor model in out-of-sample forecasting of monthly US output growth and inflation. The original NS yield factor model is to combine information (CI) of predictors and uses factors of predictors (the entire yield curve). The new supervised NS factor model is to combine forecasts (CF) and uses factors of forecasts of output growth or inflation conditional on each point of the yield curve. We formalize the concept of supervision, and demonstrate, both analytically and numerically, how supervision works. For both CF and CI schemes, principal components (PC) may also be used in place of the NS factors. In out-of-sample forecasting of U.S. monthly output growth and inflation, we find that supervised CF-factor models (CF-NS and CF-PC) are substantially better than unsupervised CI-factor models (CI-NS and CI-PC), especially at longer forecast horizons.

Suggested Citation

  • Tae-Hwy Lee & Eric Hillebrand & Huiyu Huang & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Working Papers 201903, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:201903
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    2. Cepni, Oguzhan & Gupta, Rangan & Karahan, Cenk C. & Lucey, Brian, 2022. "Oil price shocks and yield curve dynamics in emerging markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 613-623.
    3. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2020. "The Role of Oil and Risk Shocks in the High-Frequency Movements of the Term Structure of Interest Rates of the United States," Working Papers 202063, University of Pretoria, Department of Economics.
    4. Bouri, Elie & Gupta, Rangan & Majumdar, Anandamayee & Subramaniam, Sowmya, 2021. "Time-varying risk aversion and forecastability of the US term structure of interest rates," Finance Research Letters, Elsevier, vol. 42(C).
    5. Han, Yang & Jiao, Anqi & Ma, Jun, 2021. "The predictive power of Nelson–Siegel factor loadings for the real economy," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 95-127.
    6. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    7. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Sowmya Subramaniam, 2020. "High-Frequency Movements of the Term Structure of Interest Rates of the United States: The Role of Oil Market Uncertainty," Working Papers 202085, University of Pretoria, Department of Economics.
    8. Gupta, Rangan & Subramaniam, Sowmya & Bouri, Elie & Ji, Qiang, 2021. "Infectious disease-related uncertainty and the safe-haven characteristic of US treasury securities," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 289-298.

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    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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