We introduce a long-memory dynamic Tobit model, defining it as a censored version of a fractionally-integrated Gaussian ARMA model, which may include seasonal components and/or additional regression variables. Parameter estimation for such a model using standard techniques is typically infeasible, since the model is not Markovian, cannot be expressed in a finite-dimensional state-space form, an...

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...

This research develops a mixture regression model that is shown to have advantages over the classical Tobit model in model fit and predictive tests when data are generated from a two step process. Additionally, the model is shown to allow for flexibility in distributional assumptions while nesting the classic Tobit model. A simulated data set is utilized to assess the potential loss in efficien...

It is not unusual for the response variable in a regression model to be subject to censoring or truncation. Tobit regression models are specific examples of such a situation, where for some observations the observed response is not the actual response, but the censoring value (often zero), and an indicator that censoring (from below) has occurred. It is well-known that the maximum likelihood es...

In this paper I propose a computationally practical simulation estimator for large categories of the dynamic panel Tobit model with complicated dependence structures. I first apply the sequential decomposition methods introduced by Hendry and Richard (1992) to obtain the tractable simulated log-likelihood function of the dynamic panel Tobit model. I then maximize this log-likelihood function si...