IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v68y2025i1d10.1007_s00181-024-02646-4.html
   My bibliography  Save this article

Random Forest estimation of the ordered choice model

Author

Listed:
  • Michael Lechner

    (Swiss Institute for Empirical Economic Research, University of St.Gallen (SEW-HSG)
    CEPR
    CESifo
    IAB)

  • Gabriel Okasa

    (Swiss Institute for Empirical Economic Research, University of St.Gallen (SEW-HSG)
    Swiss National Science Foundation (SNSF))

Abstract

In this paper we develop a new machine learning estimator for ordered choice models based on the Random Forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with nonlinearities and high correlation among covariates. An empirical application contrasts the estimation of marginal effects and their standard errors with an Ordered Logit model. A software implementation of the Ordered Forest is provided both in R and Python in the package orf available on CRAN and PyPI, respectively.

Suggested Citation

  • Michael Lechner & Gabriel Okasa, 2025. "Random Forest estimation of the ordered choice model," Empirical Economics, Springer, vol. 68(1), pages 1-106, January.
    • Handle: RePEc:spr:empeco:v:68:y:2025:i:1:d:10.1007_s00181-024-02646-4
    DOI: 10.1007/s00181-024-02646-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-024-02646-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-024-02646-4?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.

    More about this item

    Keywords

    Ordered choice models; Random Forests; Probabilities; Marginal effects; Machine learning;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

    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:spr:empeco:v:68:y:2025:i:1:d:10.1007_s00181-024-02646-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.