To improve the detection of the economic ”danger zones” from which severe banking crises emanate, this paper introduces classification tree ensembles to the banking crisis forecasting literature. I show that their out-of-sample performance in forecasting binary banking crisis indicators surpasses current best-practice early warning systems based on logit models by a substantial margin. I obtain...

This article presents a general approach for logit random effects modelling of clustered ordinal and nominal responses. We review multinomial logit random effects models in a unified form as multivariate generalized linear mixed models. Maximum likelihood estimation utilizes adaptive Gauss–Hermite quadrature within a quasi-Newton maximization algorithm. For cases in which this is computationall...

Several new estimators of the marginal likelihood for complex non-Gaussian models are developed. These estimators make use of the output of auxiliary mixture sampling for count data and for binary and multinomial data. One of these estimators is based on combiningChib’s estimatorwith data augmentation as in auxiliarymixture sampling,while the other estimators are importance sampling and bridge ...

DCM (Discrete Choice Models) is a package, written in Ox, for estimating a class of discrete choice models. DCM represents an important development for both the OxMetric and, more generally, microeconometric computing environment in making available a broad range of discrete choice models, including standard binary response models, with notable extensions including conditional mixed logit, mixe...