Blinder-Oaxaca Decomposition for Tobit Models In this paper, a decomposition method for Tobit-models is derived, which allows the differences in a censored outcome variable between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part attributable to differences in the estimated coefficients. The method is applied to a decomposition of t...

..........................................................................................................73 Introduction .....................................................................................................74 Methods ..........................................................................................................78 Participants and Data Collection ........................

In order to meet two key objectives, enrollment managers at colleges and universities make extensive use of single equation probability models. The first objective is to generate sufficient financial resources to educate the students enrolled. The more dependent the institution is on tuition revenues, the more important is this objective. The second objective is to distribute and monitor studen...

This paper explores the dynamics of smallholder technology adoption, with particular reference to a high-yielding, low-external input rice production method in Madagascar. We present a simple model of technology adoption by farm households in an environment of incomplete financial and land markets. We then use a probit model and a symmetrically trimmed least squares estimation of a dynamic Tobi...

Since Durbin (1954) and Sargan (1958), instrumental variable (IV) method has long been one of the most popular procedures among economists and other social scientists to handle linear models with errors-in-variables. A direct application of this method to nonlinear errors-in-variables models, however, fails to yield consistent estimators. This article restricts attention to Tobit and Probit mod...

In several regression applications, a different structural relationship might be anticipated for the higher or lower responses than the average responses. In such cases, quantile regression analysis can uncover important features that would likely be overlooked by traditional mean regression. We develop a Bayesian method for fully nonparametric model-based quantile regression. The approach invo...

This paper provides a computationally practical simulation estimation for the dynamic panel Tobit model with large categories of dependence structures. The simulation estimators are conducted through correlated random effects approach. The log-likelihood function is simulated and maximized through procedures based on a recursive algorithm formulated by GHK and Gibbs sampling simulators. The ini...