IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v24y2020i1p16n4.html
   My bibliography  Save this article

A model for ordinal responses with heterogeneous status quo outcomes

Author

Listed:
  • Sirchenko Andrei

    (University of Amsterdam, Amsterdam School of Economics, Amsterdam, Netherlands)

Abstract

The decisions to reduce, leave unchanged, or increase a choice variable (such as policy interest rates) are often characterized by abundant status quo outcomes that can be generated by different processes. The decreases and increases may also be driven by distinct decision-making paths. Neither conventional nor zero-inflated models for ordinal responses adequately address these issues. This paper develops a flexible endogenously switching model with three latent regimes, which create separate processes for interest rate hikes and cuts and overlap at a no-change outcome, generating three different types of status quo decisions. The model is not only favored by statistical tests but also produces economically more meaningful inference with respect to the existing models, which deliver biased estimates in the simulations.

Suggested Citation

  • Sirchenko Andrei, 2020. "A model for ordinal responses with heterogeneous status quo outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-16, February.
    • Handle: RePEc:bpj:sndecm:v:24:y:2020:i:1:p:16:n:4
    DOI: 10.1515/snde-2018-0059
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/snde-2018-0059
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/snde-2018-0059?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. Jan Willem Nijenhuis, 2021. "Estimation of ordered probit model with endogenous switching between two latent regimes," 2021 Stata Conference 22, Stata Users Group.
    2. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.
    3. Greene, William & Harris, Mark N. & Knott, Rachel & Rice, Nigel, 2023. "Reporting heterogeneity in modeling self-assessed survey outcomes," Economic Modelling, Elsevier, vol. 124(C).

    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:bpj:sndecm:v:24:y:2020:i:1:p:16:n: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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.