IDEAS home Printed from https://ideas.repec.org/p/isu/genres/12429.html
   My bibliography  Save this paper

Bayesian Analysis of Treatment Effects in an Ordered Potential Outcomes Model

Author

Listed:
  • Li, Mingliang
  • Tobias, Justin

Abstract

We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely {\it data augmentation}, the {\it Gibbs sampler} and the {\it Metropolis-Hastings algorithm}, can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Several computational strategies which allow for non-Normality are also discussed. Conventional ``treatment effects'' such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are derived for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects.

Suggested Citation

  • Li, Mingliang & Tobias, Justin, 2008. "Bayesian Analysis of Treatment Effects in an Ordered Potential Outcomes Model," Staff General Research Papers Archive 12429, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:12429
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Becker, Gary S & Grossman, Michael & Murphy, Kevin M, 1994. "An Empirical Analysis of Cigarette Addiction," American Economic Review, American Economic Association, pages 396-418.
    2. Gallet, Craig & Agarwal, Rajshree, 1999. "The Gradual Response of Cigarette Demand to Health Information," Bulletin of Economic Research, Wiley Blackwell, vol. 51(3), pages 259-265, July.
    3. Hamilton, James L, 1972. "The Demand for Cigarettes: Advertising, the Health Scare, and the Cigarette Advertising Ban," The Review of Economics and Statistics, MIT Press, pages 401-411.
    4. Becker, Gary S & Murphy, Kevin M, 1988. "A Theory of Rational Addiction," Journal of Political Economy, University of Chicago Press, pages 675-700.
    5. Maskus, Keith E, 1983. "Evidence on Shifts in the Determinants of the Structure of U.S. Manufacturing Foreign Trade, 1958-76," The Review of Economics and Statistics, MIT Press, pages 415-422.
    6. Fenn, Aju J. & Antonovitz, Frances & Schroeter, John R., 2001. "Cigarettes and addiction information: new evidence in support of the rational addiction model," Economics Letters, Elsevier, vol. 72(1), pages 39-45, July.
    7. Sloan, Frank A. & Smith, V. Kerry & Taylor, Donald Jr., 2002. "Information, addiction, and 'bad choices': lessons from a century of cigarettes," Economics Letters, Elsevier, vol. 77(2), pages 147-155, October.
    8. Becker, Gary S & Grossman, Michael & Murphy, Kevin M, 1994. "An Empirical Analysis of Cigarette Addiction," American Economic Review, American Economic Association, pages 396-418.
    9. Han, Aaron K & Park, Daekeun, 1989. "Testing for Structural Change in Panel Data: Application to a Study of U.S. Foreign Trade in Manufacturing Goods," The Review of Economics and Statistics, MIT Press, pages 135-142.
    10. Dufour, Jean-Marie, 1982. "Recursive stability analysis of linear regression relationships: An exploratory methodology," Journal of Econometrics, Elsevier, pages 31-76.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Munkin, Murat K. & Trivedi, Pravin K., 2008. "Bayesian analysis of the ordered probit model with endogenous selection," Journal of Econometrics, Elsevier, pages 334-348.
    2. Klaus Moeltner & Richard Woodward, 2009. "Meta-Functional Benefit Transfer for Wetland Valuation: Making the Most of Small Samples," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 42(1), pages 89-108, January.
    3. Li, Mingliang & Mumford, Kevin J. & Tobias, Justin L., 2012. "A Bayesian analysis of payday loans and their regulation," Journal of Econometrics, Elsevier, pages 205-216.

    More about this item

    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:isu:genres:12429. 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: (Curtis Balmer). General contact details of provider: http://edirc.repec.org/data/deiasus.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.