A simple nonparametric test for diagnosing nonlinearity in Tobit median regression model
نویسنده
چکیده
In many applications, the response variable is observed only when it is above or below a given threshold otherwise the threshold itself is observed. Tobit median regression model is a useful semiparametric procedure for analyzing this type of censored data. We propose a simple nonparametric test for assessing the common linearity assumption in this model. Compared to those existing methods in the literature, the new test has the advantage of allowing the alternative to be any smooth function. In addition, it does not require any knowledge of the parametric distribution of the random error. The test is asymptotically normal under the null hypothesis of linearity. A small Monte Carlo study demonstrates its performance. r 2007 Elsevier B.V. All rights reserved.
منابع مشابه
Model Checking in Tobit Regression Model via Nonparametric Smoothing
A nonparametric lack-of-fit test is proposed to check the adequacy of the presumed parametric form for the regression function in Tobit regression models by applying Zheng’s device with weighted residuals. It is shown that testing the null hypothesis for the standard Tobit regression models is equivalent to test a new null hypothesis of the classic regression models. An optimal weight function ...
متن کاملA Lack-of-Fit Test in Tobit Errors-in-Variables Regression Models
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...
متن کاملA Test on the Normality in the Tobit Model
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...
متن کاملA Bayesian Nonparametric Approach to Inference for Quantile Regression
We develop a Bayesian method for nonparametric model–based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the covariates, with posterior inference for different quantile curves emerging from the conditional response distribution given the covariates. An extension to allow for partially observed responses leads ...
متن کاملA Nonparametric Model-based Approach to Inference for Quantile Regression
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...
متن کامل