IDEAS home Printed from https://ideas.repec.org/p/sce/scecf5/319.html
   My bibliography  Save this paper

Bootstrap inference on a nonlinear time series model of advertising effects

Author

Listed:
  • Miguel A. Arranz

Abstract

This paper deals with the analysis of a nonlinear time series model of the effects of advertising. Given the nonlinear nature of the process it is not possible to rely on the asymptotic inference. Furthermore, we can not provide an (asymptotic) pivotal statistic. Our solution is the application of bootstrap techniques. In particular, we find that the double bootstrap procedure provides good results. In this case, the choice of model-based time series resampling, sieve bootstrap or moving-blocks (circular blocks) bootstrap seems to have negligible effects on the confidence intervals of the parameters.

Suggested Citation

  • Miguel A. Arranz, 2005. "Bootstrap inference on a nonlinear time series model of advertising effects," Computing in Economics and Finance 2005 319, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:319
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Keywords

    Nonlinear time series; bootstrap inference; double bootstrap;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

    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:sce:scecf5:319. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.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.