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Fast Computation and Bandwidth Selection Algorithms for Smoothing Functional Time Series

Author

Listed:
  • Bastian Schäfer

    (Paderborn University)

  • Yuanhua Feng

    (Paderborn University)

Abstract

This paper examines data-driven estimation of the mean surface in nonparamet- ric regression for huge functional time series. In this framework, we consider the use of the double conditional smoothing (DCS), an equivalent but much faster translation of the 2D-kernel regression. An even faster, but again equivalent func- tional DCS (FCDS) scheme and a boundary correction method for the DCS/FCDS is proposed. The asymptotically optimal bandwidths are obtained and selected by an IPI (iterative plug-in) algorithm. We show that the IPI algorithm works well in practice in a simulation study and apply the proposals to estimate the spot-volatility and trading volume surface in high-frequency nancial data under a functional representation. Our proposals also apply to large lattice spatial or spatial-temporal data from any research area.

Suggested Citation

  • Bastian Schäfer & Yuanhua Feng, 2021. "Fast Computation and Bandwidth Selection Algorithms for Smoothing Functional Time Series," Working Papers CIE 143, Paderborn University, CIE Center for International Economics.
  • Handle: RePEc:pdn:ciepap:143
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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP143.pdf
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    More about this item

    Keywords

    Spatial nonparametric regression; boundary correction; functional double conditional smoothing; bandwidth selection; spot volatility surface;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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