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

ABSTRACT 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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    ABSTRACT: Despite spatial econometrics is now considered a consolidated discipline, only in recent years we have experienced an increasing attention to the possibility of applying it to the field of discrete choices (e.g. Smirnov, 2010 for a recent review) and limited dependent variable models. In particular, only a small number of papers introduced the above-mentioned models in Health Economics. The main purpose of the present paper is to review the different methodological solutions in spatial discrete choice models as they appeared in several applied fields by placing an emphasis on the health economics applications.
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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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    ABSTRACT: This paper presents a Bayesian analysis of bivariate ordered probit regression model with excess of zeros. Specifically, in the context of joint modeling of two ordered outcomes, we develop zero-inflated bivariate ordered probit model and carry out estimation using Markov Chain Monte Carlo techniques. Using household tobacco survey data with substantial proportion of zeros, we analyze the socio-economic determinants of individual problem of smoking and chewing tobacco. In our illustration, we find strong evidence that accounting for excess zeros provides good fit to the data. The example shows that the use of a model that ignores zero-inflation masks differential effects covariates have on the two regimes, non-users versus users at all levels of consumption.
    Journal of Probability and Statistics 09/2011; DOI:10.1155/2012/617678
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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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    ABSTRACT: This paper proposes an algorithm for adaptive, sequential classification in systems with unknown labeling errors, focusing on the biomedical application of Brain Computer Interfacing (BCI). The method is shown to be robust in the presence of label and sensor noise. We focus on the inference and prediction of target labels under a nonlinear and non-Gaussian model. In order to handle missing or erroneous labeling, we model observed labels as a noisy observation of a latent label set with multiple classes (≥ 2). Whilst this paper focuses on the method's application to BCI systems, the algorithm has the potential to be applied to many application domains in which sequential missing labels are to be imputed in the presence of uncertainty. This dynamic classification algorithm combines an Ordered Probit model and an Extended Kalman Filter (EKF). The EKF estimates the parameters of the Ordered Probit model sequentially with time. We test the performance of the classification approach by processing synthetic datasets and real experimental EEG signals with multiple classes (2, 3 and 4 labels) for a Brain Computer Interfacing (BCI) experiment.
    Neural networks: the official journal of the International Neural Network Society 03/2011; 24(7):726-34. DOI:10.1016/j.neunet.2011.03.019 · 2.71 Impact Factor
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