IDEAS home Printed from https://ideas.repec.org/p/pdn/ciepap/143.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP143.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bastian Schäfer, 2021. "Bandwidth selection for the Local Polynomial Double Conditional Smoothing under Spatial ARMA Errors," Working Papers CIE 146, Paderborn University, CIE Center for International Economics.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pdn:ciepap:143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: WP-WiWi-Info or the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cipadde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.