IDEAS home Printed from https://ideas.repec.org/a/oec/stdkaa/5kzdnhzpzq8w.html
   My bibliography  Save this article

Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data

Author

Listed:
  • Gérard Biau
  • Olivier Biau
  • Laurent Rouvière

Abstract

A large majority of summary indicators derived from the individual responses to qualitative Business Tendency Surveys (which are mostly three-modality questions) result from standard aggregation and quantification methods. This is typically the case for the indicators called balances of opinion, which are currently used in short term analysis and considered by forecasters as explanatory variables in many models. In the present paper, we discuss a new statistical approach to forecast the manufacturing growth from firm-survey responses. We base our predictions on a forecasting algorithm inspired by the random forest regression method, which is known to enjoy good prediction properties. Our algorithm exploits the heterogeneity of the survey responses, works fast, is robust to noise and allows for the treatment of missing values. Starting from a real application on a French dataset related to the manufacturing sector, this procedure appears as a competitive method compared with traditional algorithms.

Suggested Citation

  • Gérard Biau & Olivier Biau & Laurent Rouvière, 2008. "Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(3), pages 317-331.
  • Handle: RePEc:oec:stdkaa:5kzdnhzpzq8w
    DOI: 10.1787/jbcma-v2007-art15-en
    as

    Download full text from publisher

    File URL: https://doi.org/10.1787/jbcma-v2007-art15-en
    Download Restriction: Full text available to READ online. PDF download available to OECD iLibrary subscribers.

    File URL: https://libkey.io/10.1787/jbcma-v2007-art15-en?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Alquier Pierre & Li Xiaoyin & Wintenberger Olivier, 2014. "Prediction of time series by statistical learning: general losses and fast rates," Dependence Modeling, De Gruyter, vol. 1, pages 65-93, January.
    2. Olivier BIAU & Angela D´ELIA, 2010. "Euro Area GDP Forecast Using Large Survey Dataset - A Random Forest Approach," EcoMod2010 259600029, EcoMod.

    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:oec:stdkaa:5kzdnhzpzq8w. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/oecddfr.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.