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A Survey on Nonparametric Time Series Analysis

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  • Siegfried Heiler

    (Center of Finance and Econometrics)

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  • Siegfried Heiler, 1999. "A Survey on Nonparametric Time Series Analysis," Finance 9904005, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:9904005
    Note: 49 pages
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/9904/9904005.pdf
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    References listed on IDEAS

    as
    1. Lijian Yang & Wolfgang Hardle & Jens Nielsen, 1999. "Nonparametric Autoregression with Multiplicative Volatility and Additive mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(5), pages 579-604, September.
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    Cited by:

    1. Mayte Suarez -Farinas & Carlos E. Pedreira & Marcelo C. Medeiros, 2004. "Local Global Neural Networks: A New Approach for Nonlinear Time Series Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1092-1107, December.
    2. Holtemöller, Oliver & Kozyrev, Boris, 2024. "Forecasting economic activity using a neural network in uncertain times: Monte Carlo evidence and application to the German GDP," IWH Discussion Papers 6/2024, Halle Institute for Economic Research (IWH).
    3. Norberto Rodríguez N. & Patricia Siado C., 2003. "Un Pronóstico no Paramétrico de la Inflación Colombiana," Borradores de Economia 248, Banco de la Republica de Colombia.
    4. Tao Chen & Yixuan Li & Renfang Tian, 2023. "A Functional Data Approach for Continuous-Time Analysis Subject to Modeling Discrepancy under Infill Asymptotics," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
    5. Holtemöller, Oliver & Kozyrev, Boris, 2023. "Forecasting Economic Activity with a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to German GDP," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277688, Verein für Socialpolitik / German Economic Association.

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    • G - Financial Economics

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