IDEAS home Printed from https://ideas.repec.org/a/adr/anecst/y2019i134p207-226.html
   My bibliography  Save this article

Identification in Binary Response Panel Data Models: Is Point-Identification More Common Than We Thought?

Author

Listed:
  • Bo E. Honoré
  • Ekaterini Kyriazidou

Abstract

This paper investigates identification in binary response models with panel data. Conditioning on sufficient statistics can sometimes lead to a conditional maximum likelihood approach that can be used to identify and estimate the parameters of interest in such models. Unfortunately it is often difficult or impossible to find such sufficient statistics, and even if it is possible, the approach sometimes leads to conditional likelihoods that do not depend on some interesting parameters. Using a range of different data generating processes, this paper calculates the identified regions for parameters in panel data logit AR(2) and logit VAR(1) models for which it is not known whether the parameters are identified or not. We find that identification might be more common than was previously thought, and that the identified regions for non-identified objects may be small enough to be empirically useful.

Suggested Citation

  • Bo E. Honoré & Ekaterini Kyriazidou, 2019. "Identification in Binary Response Panel Data Models: Is Point-Identification More Common Than We Thought?," Annals of Economics and Statistics, GENES, issue 134, pages 207-226.
    • Handle: RePEc:adr:anecst:y:2019:i:134:p:207-226
    DOI: 10.15609/annaeconstat2009.134.0207
    as

    Download full text from publisher

    File URL: https://www.jstor.org/stable/10.15609/annaeconstat2009.134.0207
    Download Restriction: no
    File URL: https://libkey.io/10.15609/annaeconstat2009.134.0207?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
    ---><---

    Citations

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


    Cited by:

    1. Bo E. Honor'e & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Papers 2005.05942, arXiv.org, revised Dec 2023.
    2. Carsten Jentsch & Lena Reichmann, 2022. "Generalized binary vector autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 285-311, March.
    3. Bo E. Honoré & Martin Weidner, 2021. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Working Papers 2021-79, Princeton University. Economics Department..
    4. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A Dynamic Ordered Logit Model with Fixed Effects," Department of Economics Working Papers 2020-14, McMaster University.
    5. Bo E. Honoré & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," CeMMAP working papers CWP38/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    More about this item

    Keywords

    Panel Data; Discrete Choice; Fixed Effects; Identification.;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    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:adr:anecst:y:2019:i:134:p:207-226. 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: Secretariat General or Laurent Linnemer (email available below). General contact details of provider: https://edirc.repec.org/data/ensaefr.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.