IDEAS home Printed from
   My bibliography  Save this paper

Bayesian Analysis of Econometrics Systems with Discrete Variables and Inequality Constraints


  • Asli Ogunc

    () (Louisiana State University)

  • Dek Terrell

    () (Louisiana State University)

  • R. Carter Hill

    () (Louisiana State University)


In econometric models, sign or inequality constraints on parameters arise in a wide variety of applications. In this paper, we incorporate linear and nonlinear inequality constraints into systems of equations with both discrete and continuous variables serving as the dependent variables. The posterior sample is generated using Gibbs and Metropolis algorithms with data augmentation. One illustrative example analyzes the choice between fixed and adjustable mortgage rates with inequality restrictions on the financial and borrower characteristics. A second application focuses on certification and performance of auditors.

Suggested Citation

  • Asli Ogunc & Dek Terrell & R. Carter Hill, 1999. "Bayesian Analysis of Econometrics Systems with Discrete Variables and Inequality Constraints," Computing in Economics and Finance 1999 834, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:834

    Download full text from publisher

    File URL:
    File Function: main text
    Download Restriction: no

    References listed on IDEAS

    1. Beard, T Randolph & Caudill, Steven B & Gropper, Daniel M, 1991. "Finite Mixture Estimation of Multiproduct Cost Functions," The Review of Economics and Statistics, MIT Press, vol. 73(4), pages 654-664, November.
    2. Kon, Stanley J & Jen, Frank C, 1978. "Estimation of Time-Varying Systematic Risk and Performance for Mutual Fund Portfolios: An Application of Switching Regression," Journal of Finance, American Finance Association, vol. 33(2), pages 457-475, May.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:sce:scecf9:834. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.