Specification Errors in Limited Dependent Variable Models

A preliminary investigation of two specification error problems in truncated dependent variable models is reported. It is shown that heteroscedasticity in a tobit model results in biased estimates when the model is misspecified. This differs from the OLS model where estimates are still consistent though inefficient. The second problem examined is aggregation. An appropriate nonlinear least squares regression model is derived for situations when the micro-level model fits a tobit framework but only aggregate data are available.