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Bayesian Analysis of Structural Effects in an Ordered Equation System

Author

Listed:
  • Li Mingliang

    (State University of New York at Buffalo)

  • Tobias Justin L

    (Iowa State University)

Abstract

We describe a new simulation-based algorithm for Bayesian estimation of structural effects in models where the outcome of interest and an endogenous treatment variable are ordered. Our algorithm makes use of a reparameterization, suggested by Nandram and Chen (1996) in the context of a single equation ordered-probit model, which significantly improves the mixing of the standard Gibbs sampler. We illustrate the improvements afforded by this new algorithm (relative to the standard Gibbs sampler) in a generated data experiment and also make use of our methods in an empirical application. Specifically, we take data from the National Longitudinal Survey of Youth (NLSY) and investigate the impact of maternal alcohol consumption on early infant health. Our results show clear evidence that the health outcomes of infants whose mothers drink while pregnant are worse than the outcomes of infants whose mothers never consumed alcohol while pregnant. In addition, the estimated parameters clearly suggest the need to control for the endogeneity of maternal alcohol consumption.

Suggested Citation

  • Li Mingliang & Tobias Justin L, 2006. "Bayesian Analysis of Structural Effects in an Ordered Equation System," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(4), pages 1-24, December.
    • Handle: RePEc:bpj:sndecm:v:10:y:2006:i:4:n:7
    DOI: 10.2202/1558-3708.1363
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    3. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    4. Rong Zhang & Brett A. Inder & Xibin Zhang, 2012. "Parameter estimation for a discrete-response model with double rules of sample selection: A Bayesian approach," Monash Econometrics and Business Statistics Working Papers 5/12, Monash University, Department of Econometrics and Business Statistics.
    5. Mingliang Li & Justin L. Tobias, 2006. "Calculus attainment and grades received in intermediate economic theory," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 893-896, September.
    6. Anastasios Panagiotelis & Michael S. Smith & Peter J. Danaher, 2014. "From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence, and Visit Behavior," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 14-29, January.
    7. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010. "A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function," Monash Econometrics and Business Statistics Working Papers 3/10, Monash University, Department of Econometrics and Business Statistics.
    8. Zhang, Rong & Inder, Brett A. & Zhang, Xibin, 2015. "Bayesian estimation of a discrete response model with double rules of sample selection," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 81-96.
    9. Rong Zhang & Brett A. Inder & Xibin Zhang, 2013. "Bayesian estimation of a discrete response model with double rules of sample selection," Monash Econometrics and Business Statistics Working Papers 24/13, Monash University, Department of Econometrics and Business Statistics.

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