The Effect of Ignoring Heteroscedasticity on Estimates of the Tobit Model
We consider the sensitivity of the Tobit estimator to heteroscedasticity. Our single independent variable is a dummy variable whose coefficient is a difference between group means, and the error variance differs between groups. Heteroscedasticity biases the Tobit estimate of the two means in opposite directions, so the bias in estimating their difference can be significant. This bias is not monotonically related to the true difference, and is greatly increased if the limit observations are not available. Perhaps surprisingly, the Tobit estimates are sometimes more severely biased than are OLS estimates.
|Date of creation:||Jan 1983|
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- James Tobin, 1956. "Estimation of Relationships for Limited Dependent Variables," Cowles Foundation Discussion Papers 3R, Cowles Foundation for Research in Economics, Yale University.
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