IDEAS home Printed from https://ideas.repec.org/p/pdn/ciepap/102.html
   My bibliography  Save this paper

Data-driven local polynomial for the trend and its derivatives in economic time series

Author

Listed:
  • Yuanhua Feng

    (Paderborn University)

  • Thomas Gries

    (Paderborn University)

Abstract

The main purpose of this paper is the development of iterative plug-in algorithms for local polynomial estimation of the trend and its derivatives in macroeconomic time series. In particular, a data-driven lag-window estimator for the variance factor is proposed so that the bandwidth is selected without any parametric assumption on the stationary errors. Further analysis of the residuals using an ARMA model is discussed briefl y. Moreover, confidence bounds for the trend and its derivatives are conducted using some asymptotically unbiased estimates and applied to test possible linearity of the trend. These graphical tools also provide us further detailed features about the economic development. Practical performance of the proposals is illustrated by quarterly US and UK GDP data.

Suggested Citation

  • Yuanhua Feng & Thomas Gries, 2017. "Data-driven local polynomial for the trend and its derivatives in economic time series," Working Papers CIE 102, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:102
    as

    Download full text from publisher

    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP102.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Sebastian Letmathe, 2022. "Data-driven P-Splines under short-range dependence," Working Papers CIE 152, Paderborn University, CIE Center for International Economics.
    2. Feng, Yuanhua & Härdle, Wolfgang Karl, 2020. "A data-driven P-spline smoother and the P-Spline-GARCH models," IRTG 1792 Discussion Papers 2020-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 125, Paderborn University, CIE Center for International Economics.
    4. Xuehai Zhang, 2019. "A Box-Cox semiparametric multiplicative error model," Working Papers CIE 122, Paderborn University, CIE Center for International Economics.

    More about this item

    Keywords

    Macroeconomic time series; semiparametric modelling; nonparametric regression with dependent errors; bandwidth selection; misspecification test;
    All these keywords.

    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:102. 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.