Showing papers on "Tobit model published in 1981"

On the asymptotic bias of the ordinary least squares estimator of the tobit model

Abstract: This paper presents a precise characterization of the bias of least squares in two limited dependent variable models, the Tobit model and the truncated regression model. For the cases considered, the method of moments can be used to correct the bias of OLS. For more general cases, the results provide approximations which appear to be relatively robust. 13, and o, . In this paper we present a precise characterization of that bias for the particular case in which xt, as well as -,, is normally distributed. We also show that the bias of the OLS slope estimator can be corrected by dividing each estimate by the sample proportion of nonlimit observations. Other structural parameters can be consistently estimated in a similar fashion. We present some evidence on the effect of nonnormality with respect to the predictions obtained in the normal model. The case in which the sample contains only nonlimit observation (the truncated regression model) is considered elsewhere (Olsen (7)). We analyze the relationship between his results and ours, and derive some predictions of the normal model with respect to the seriousness of "truncation bias."

A Test For Heteroscedasticity in a Limited Dependent Variable Model

Abstract: Summary Limited Dependent Variable models arise, for example, when the dependent variable is necessarily non-negative. When these models are estimated under the incorrect assumption of homoscedasticity, serious consequences have been found. It is therefore important to test for its existence. In this paper, we make use of the Lagrange multiplier procedure to derive a test for heteroscedasticity in the Tobit model.