Bootstrapping a Conditional Moments Test for Normality after Tobit Estimation
By D. Drukker. 2002
74 citations recorded by the source corpus.
Categorical and limited dependent variable models are routinely estimated via maximum likelihood. It is well-known that the ML estimates of the parameters are inconsistent if the distribution or the skedastic component is misspecified. When conditional moment tests were first developed by Newey (1985) and Tauchen (1985), they appeared to offer a wide range of easy-to-compute specification tests for categorical and limited dependent variable models estimated by maximum likelihood. However, subsequent studies found that using the asymptotic critical values produced severe size distortions. This paper presents simulation evidence that the standard conditional moment test for normality after tobit estimation has essentially no size distortion and reasonable power when the critical values are obtained via a parametric bootstrap.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/9db88efdabbb1ba639999a7aa4ddec33ca898964
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Citations in the corpus
- Uncertainty of Governmental Relief and the Crowding out of Flood Insurance (2013, methodology): “To diagnose the validity of this assumption we apply an parametric bootstrap version (Drukker 2002) of the conditional moment test proposed by Skeels & Vella (1999). If the test rejects the null of normality we have to apply an alternative to the Tobit model.”
- Tobit models in strategy research: Critical issues and applications (2019, methodology): “…transformation to the dependent variable (Laursen & Salter, 2006).25 More formally, scholars may use the Stata command tobcm to implement a bootstrap-based conditional moment test on the null hypothesis that the residuals are Normal (for more details see Skeels & Vella, 1999 and Drukker, 2002).”