IDEAS home Printed from https://ideas.repec.org/p/fip/fedmwp/552.html
   My bibliography  Save this paper

Bayesian inference for linear models subject to linear inequality constraints

Author

Listed:
  • John F. Geweke

Abstract

The normal linear model, with sign or other linear inequality constraints on its coefficients, arises very commonly in many scientific applications. Given inequality constraints Bayesian inference is much simpler than classical inference, but standard Bayesian computational methods become impractical when the posterior probability of the inequality constraints (under a diffuse prior) is small. This paper shows how the Gibbs sampling algorithm can provide an alternative, attractive approach to inference subject to linear inequality constraints in this situation, and how the GHK probability simulator may be used to assess the posterior probability of the constraints.

Suggested Citation

  • John F. Geweke, 1995. "Bayesian inference for linear models subject to linear inequality constraints," Working Papers 552, Federal Reserve Bank of Minneapolis.
  • Handle: RePEc:fip:fedmwp:552
    as

    Download full text from publisher

    File URL: http://www.minneapolisfed.org/research/common/pub_detail.cfm?pb_autonum_id=610
    Download Restriction: no

    File URL: http://www.minneapolisfed.org/research/WP/WP552.pdf
    Download Restriction: no

    Citations

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


    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. Andersson, Michael K. & Palmqvist, Stefan & Waggoner, Daniel F., 2010. "Density-Conditional Forecasts in Dynamic Multivariate Models," Working Paper Series 247, Sveriges Riksbank (Central Bank of Sweden).
    3. Paul Knottnerus, 2016. "On new variance approximations for linear models with inequality constraints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(1), pages 26-46, February.
    4. Qian, Hang, 2010. "Linear regression using both temporally aggregated and temporally disaggregated data: Revisited," MPRA Paper 32686, University Library of Munich, Germany.
    5. Golan, Amos & Judge, George & Perloff, Jeffrey, 1997. "Estimation and inference with censored and ordered multinomial response data," Journal of Econometrics, Elsevier, vol. 79(1), pages 23-51, July.

    More about this item

    Keywords

    Econometric models;

    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:fip:fedmwp:552. 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: (Jannelle Ruswick). General contact details of provider: http://edirc.repec.org/data/frbmnus.html .

    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.