Bayesian analysis of the ordered probit model with endogenous selection

Department of Economics, Indiana University, Wylie Hall 105, Bloomington, IN 47405, USA
Journal of Econometrics (Impact Factor: 1.6). 04/2008; 143(2):334-348. DOI: 10.1016/j.jeconom.2007.11.001


This paper presents a Bayesian analysis of an ordered probit model with endogenous selection. The model can be applied when analyzing ordered outcomes that depend on endogenous covariates that are discrete choice indicators modeled by a multinomial probit model. The model is illustrated by analyzing the effects of different types of medical insurance plans on the level of hospital utilization, allowing for potential endogeneity of insurance status. The estimation is performed using the Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution of the parameters in the model.

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Available from: Murat K. Munkin, Sep 08, 2015
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    • "g . Varin and Czado , 2010 ; Munkin and Trivedi , 2008 ; Contoyannis et al . , 2004 ; Lindeboom and van Dooslaer , 2004 ; van Dooslaer and Jones , 2003 ; Groot , 2000 among many others )  Multinomial probit / logit models ( e . "
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    • "The underlying response variables could be measured on an ordinal scale. It is also common in the literature to generate a categorical or grouped variable from an underlying quantitative variable, and then use ordinal response regression model (e.g., Biswas and Das [2], Butler and Chatterjee [3], and Munkin and Trivedi [23]. The ensuing model is usually analyzed using the bivariate ordered probit model. "
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    • "For an ordinal dataset, the basic concept of the Ordered Probit model is that there is a latent continuous metric underlying the ordinal responses, and that thresholds partition the cumulative distribution function (c.d.f.) into a series of regions corresponding to the ordinal categories. Most literature in the Ordered Probit model focuses on off-line estimation of the thresholds for partitioning and on the estimation of their parameters and hyper-parameters (using, for example, Maximum Likelihood, Markov Chain Monte Carlo and Gibbs sampling) [Ronning and Kukuk, 1996; Munkin and Trivedi, 2007]. However, the metrics (the thresholds) for ordinal classification may be adapted rather than kept fixed. "
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