Abstract
Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross-validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.
Original language | English |
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Pages (from-to) | 324-339 |
Number of pages | 16 |
Journal | Econometrics Journal |
Volume | 12 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2009 |
Externally published | Yes |
Keywords
- Bayes factor
- Cross-validation
- Deviance information criterion
- Marginal effects
- Marginal likelihood
- Markov chain Monte Carlo
- ROC curve