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On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter

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  • Poirier, Dale J
  • Tobias, Justin

Abstract

In this article we describe methods for obtaining the predictive distributions of outcome gains in the framework of a standard latent variable selection model. Although most previous work has focused on estimation of mean treatment parameters as the method for characterizing outcome gains from program participation, we show how the entire distributions associated with these gains can be obtained in certain situations. Although the out-of sample outcome gain distributions depend on an unidentified parameter, we use the results of Koop and Poirier to show that learning can take place about this parameter through information contained in the identified parameters via a positive definiteness restriction on the covariance matrix.

Suggested Citation

  • Poirier, Dale J & Tobias, Justin, 2003. "On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter," Staff General Research Papers Archive 12014, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:12014
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    Cited by:

    1. Giorgio Calzolari & Antonino Di Pino, 2017. "Self-selection and direct estimation of across-regime correlation parameter," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2142-2160, September.
    2. Ariun Ishdorj & Mary Kay Crepinsek & Helen H. Jensen, 2013. "Children's Consumption of Fruits and Vegetables: Do School Environment and Policies Affect Choices at School and Away from School?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 35(2), pages 341-359.
    3. Munkin, Murat K. & Trivedi, Pravin K., 2008. "Bayesian analysis of the ordered probit model with endogenous selection," Journal of Econometrics, Elsevier, vol. 143(2), pages 334-348, April.
    4. Muto, Sachio, 2006. "Estimation of the bid rent function with the usage decision model," Journal of Urban Economics, Elsevier, vol. 60(1), pages 33-49, July.
    5. Li, Mingliang & Tobias, Justin L., 2011. "Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling," Journal of Econometrics, Elsevier, vol. 162(2), pages 345-361, June.
    6. Chib, Siddhartha, 2007. "Analysis of treatment response data without the joint distribution of potential outcomes," Journal of Econometrics, Elsevier, vol. 140(2), pages 401-412, October.
    7. Ishdorj, Ariun & Crepinsek, Mary Kay & Jensen, Helen H., 2012. "Children’s Consumption of Fruits and Vegetables: Do School Environment and Policies Affect Choice in School Meals?," 2012 AAEA/EAAE Food Environment Symposium, May 30-31, Boston, MA 123534, Agricultural and Applied Economics Association.
    8. Shimeles Abebe & Andinet Woldemichael, 2015. "Working Paper 225 - Measuring the Impact of Micro-Health Insurance on Healthcare Utilization: A Bayesian Potential Outcomes Approach," Working Paper Series 2166, African Development Bank.
    9. Anders Løland & Ragnar Bang Huseby & Nils Lid Hjort & Arnoldo Frigessi, 2013. "Statistical Corrections of Invalid Correlation Matrices," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 807-824, December.
    10. Ishdorj, Ariun & Jensen, Helen H. & Tobias, Justin, 2007. "Intra-Household Allocation and Consumption of WIC-Approved Foods: A Bayesian Approach," Staff General Research Papers Archive 12833, Iowa State University, Department of Economics.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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