IDEAS home Printed from https://ideas.repec.org/a/bpj/jecome/v11y2022i1p55-70n6.html
   My bibliography  Save this article

Biases in Maximum Simulated Likelihood Estimation of Bivariate Models

Author

Listed:
  • Jumamyradov Maksat

    (Department of Economics, University of South Florida, Tampa, FL, USA)

  • Munkin Murat K.

    (Department of Economics, University of South Florida, Tampa, FL, USA)

Abstract

This paper finds that the maximum simulated likelihood (MSL) estimator produces substantial biases when applied to the bivariate normal distribution. A specification of the random parameter bivariate normal model is considered, in which a direct comparison between the MSL and maximum likelihood (ML) estimators is feasible. The analysis shows that MSL produces biased results for the correlation parameter. This paper also finds that the MSL estimator is biased for the bivariate Poisson-lognormal model, developed by Munkin and Trivedi (1999. “Simulated Maximum Likelihood Estimation of Multivariate Mixed-Poisson Regression Models, with Application.” The Econometrics Journal 2: 29–48). A simulation study is conducted, which shows that MSL leads to serious inferential biases, especially large when variance parameters in the true data generating process are small. The MSL estimator produces biases in the estimated marginal effects, conditional means and probabilities of count outcomes.

Suggested Citation

  • Jumamyradov Maksat & Munkin Murat K., 2022. "Biases in Maximum Simulated Likelihood Estimation of Bivariate Models," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 55-70, January.
    • Handle: RePEc:bpj:jecome:v:11:y:2022:i:1:p:55-70:n:6
    DOI: 10.1515/jem-2021-0003
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jem-2021-0003
    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/jem-2021-0003?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

    Maximum Simulated Likelihood; Simulation Biases; Bivariate Models;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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:bpj:jecome:v:11:y:2022:i:1:p:55-70:n:6. 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.