IDEAS home Printed from https://ideas.repec.org/c/sus/susesa/0213.html
 

Matlab code for bivariate Gaussian kernel regression

Author

Listed:
  • Richard Tol

    (Department of Economics, University of Sussex)

Programming Language

Matlab

Abstract

Four Matlab scripts that scrape data on individual economists from citec.repec.org (processperson.m and processextraperson.m), reorganize that data (postprocessperson.m), and estimate the strength of the Matthew effect per cohort (analyzeperson2.m).

Suggested Citation

  • Richard Tol, 2013. "Matlab code for bivariate Gaussian kernel regression," Economics Software Archive 0213, Department of Economics, University of Sussex Business School.
  • Handle: RePEc:sus:susesa:0213
    as

    Download full text from publisher

    File URL: http://www.sussex.ac.uk/economics/documents/gaussiankernelregression.zip
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Matlab;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

    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:sus:susesa:0213. 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: University of Sussex Business School Communications Team (email available below). General contact details of provider: https://edirc.repec.org/data/ecsusuk.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.