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

Author

Listed:
  • Poirier, D.J.
  • Tobias, J.L.

Abstract

In this paper we describe methods for obtaining the predictive distributions of outcome gains in the framework of a standard latent variable selection model. While most previous work has focused on estimation of mean treatment parameters as the method for characterizing outcome gains from program participation, we show the entire distributions associated with these gains can be accurately obtained on certain situations.

Suggested Citation

  • Poirier, D.J. & Tobias, J.L., 2001. "On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter," Papers 00-01-30, California Irvine - School of Social Sciences.
    • Handle: RePEc:fth:calirv:00-01-30
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Mingliang Li & Dale J. Poirier & Justin L. Tobias, 2004. "Do dropouts suffer from dropping out? Estimation and prediction of outcome gains in generalized selection models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(2), pages 203-225.
    4. Becher, Michael & Stegmueller, Daniel, 2019. "Cognitive Ability, Union Membership, and Voter Turnout," IAST Working Papers 19-97, Institute for Advanced Study in Toulouse (IAST).
    5. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, John Wiley & Sons, vol. 68(2), pages 210-223, October.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Olivier De Groote & Koen Declercq, 2021. "Tracking and specialization of high schools: Heterogeneous effects of school choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 898-916, November.
    11. 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.
    12. Kris J. Mitchener & Angela Vossmeyer & Kris James Mitchener, 2023. "How Do Financial Crises Redistribute Risk?," CESifo Working Paper Series 10597, CESifo.
    13. Murray D. Smith, 2005. "Using Copulas to Model Switching Regimes with an Application to Child Labour," The Economic Record, The Economic Society of Australia, vol. 81(s1), pages 47-57, August.
    14. 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 123534, Agricultural and Applied Economics Association.
    15. Xiaoyi Han & Lung-Fei Lee, 2016. "Bayesian Analysis of Spatial Panel Autoregressive Models With Time-Varying Endogenous Spatial Weight Matrices, Common Factors, and Random Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 642-660, October.
    16. Giorgio Calzolari & Maria Gabriella Campolo & Antonino Pino & Laura Magazzini, 2023. "Assessing individual skill influence on housework time of Italian women: an endogenous-switching approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 659-679, June.
    17. Martijn van Hasselt & Christopher R. Bollinger & Jeremy W. Bray, 2022. "A Bayesian approach to account for misclassification in prevalence and trend estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 351-367, March.
    18. Justin Tobias, 2006. "Estimation, Learning and Parameters of Interest in a Multiple Outcome Selection Model," Econometric Reviews, Taylor & Francis Journals, vol. 25(1), pages 1-40.
    19. Murat K. Munkin & Partha Deb & Pravin K. Trivedi, 2006. "Bayesian analysis of the two-part model with endogeneity: application to health care expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 1081-1099.
    20. Murat K. Munkin, 2022. "Count Roy model with finite mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1160-1181, September.
    21. 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.
    22. 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.

    More about this item

    Keywords

    DISTRIBUTION ; OUTCOME ; INFORMATION;
    All these keywords.

    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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