Bootstrapping a conditional moments test for normality after tobit estimation
Abstract. 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.
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David M. Drukker
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conditional moment tests, bootstrap, tobit, normality
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