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Forecasting the US Term Structure of Interest Rates Using Nonparametric Functional Data Analysis

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  • JoÃo Caldeira
  • Hudson Torrent

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  • JoÃo Caldeira & Hudson Torrent, 2017. "Forecasting the US Term Structure of Interest Rates Using Nonparametric Functional Data Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 56-73, January.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:1:p:56-73
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    Cited by:

    1. Christoph Berninger & Almond Stöcker & David Rügamer, 2022. "A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 181-200, January.
    2. Eppelsheimer, Johann & Jahn, Elke J. & Rust, Christoph, 2022. "The spatial decay of human capital externalities - A functional regression approach with precise geo-referenced data," Regional Science and Urban Economics, Elsevier, vol. 95(C).
    3. João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
    4. João F. Caldeira & Rangan Gupta & Hudson S. Torrent, 2020. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    5. Cees Diks & Bram Wouters, 2023. "Noise reduction for functional time series," Papers 2307.02154, arXiv.org.

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