DCM (Discrete Choice Models) is a package for estimating a class of discrete choice models. DCM is a class written in Ox, that implements a wide range of discrete choice models including standard binary response models, with notable extensions including conditional mixed logit, mixed probit, multinomial probit, and random coefficient ordered choice models. The current version can handle both cr...

Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates, and even estimated cor...

Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial ordered probit (DSOP) model in order to capture...

Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be as important as the classification itself. In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior di...

Development of statistical methods and software for mapping interacting QTL has been the focus of much recent research. We previously developed a Bayesian model selection framework, based on the composite model space approach, for mapping multiple epistatic QTL affecting continuous traits. In this study we extend the composite model space approach to complex ordinal traits in experimental cross...

Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial ordered probit (DSOP) model in order to capture...

Abstract This paper derives a new decomposition of stock returns using price extremes and proposes conditional autoregressive shape (CARS) model with beta density to predict the direction returns. The CARS is continuously valued, which makes it different from binary classification models. An empirical study performed on US market, results show that predicting power not only statistically signif...

We discuss the application of the GHK simulation method for maximum likelihood estimation of the multivariate probit regression model and describe and illustrate a Stata program mvprobit for this purpose.

Based on a unique firm-level data set from the German manufacturing sector, this paper disentangles environmental and non-environmental product and process innovations. The multivariate probit analysis shows that the various innovation types are determined by different factors. The estimation results suggest a policy mix which comprises the encouragement of R&D activities, certified management ...

GENDER AND RACIAL DISCRIMINATION IN PAY AND PROMOTION FOR NHS NURSES For many years the NHS has been subject to allegations that gender and racial discrimination are a feature of the internal labour market for qualified nurses. This paper examines this issue with regard to the promotion process using 1994 survey data. We start by rejecting the assumption of covariate exogeneity inherent in the ...