Complex longitudinal data are commonly analyzed using nonlinearmixed-effects NLME models with a normal distribution. However, a departure fromnormalitymay lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures ca...

The problem of fitting a parametric model in Tobit errors-in-variables regression models is discussed in this paper. The proposed test is based on the supremum of the Khamaladze type transformation of a certain partial sum process of calibrated residuals. This framework covers the usual error-free Tobit model as a special case. The asymptotic null distribution of this transformed process is sho...

This paper uses Tobit models and data for union contracts to examine the extent of downward nominal-wage rigidity in Canada. To be consistent with important stylized facts, the models allow the variance of the notional wage-change distribution to be time-varying and test for menu-cost effects. The empirical results confirm the importance of using a general specification with a timechanging vari...

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...

We demonstrate how censored regression models (including standard Tobit models) can be estimated in R using the add-on package censReg. This package provides not only the usual maximum likelihood (ML) procedure for cross-sectional data but also the random-effects maximum likelihood procedure for panel data using Gauss-Hermite quadrature.

The nonlinear fixed effects model has two shortcomings, one practical, one methodological. The practical obstacle relates to the difficulty of estimating nonlinear models with possibly thousands of dummy variable coefficients. In fact, in many models of interest to practitioners, estimation of the fixed effects model is feasible even in panels with very large numbers of groups. The result, thou...

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...

Background. Clinical trials in cancer frequently include cancer-specific measures of health but not preferencebased measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting E...

BACKGROUND Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting E...

We propose an alternative Hausman test for normality in the Tobit model. Unlike the previous tests by Ruud (1984) or Newey (1987), our test compares the Tobit estimator to an estimator which is consistent both under the null hypothesis and under the alternative hypothesis. To do so, it utilizes a purely nonparametric estimator for the Tobit model proposed by Newey (1999b) and Jeong (2004). We p...