Heteroskedastic Transformation Models with Covariate Dependent Censoring
نویسندگان
چکیده
In this paper we propose an inferential procedure for transformation models with conditional heteroskedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form. We provide sufficient conditions for point identification. We then propose a consistent estimator and show that it is asymptoticaly √ n normal. We conduct a simulation study that reveals adequate finite sample performance. We also use the estimator in an empirical illustration, where we estimate the effect of UI benefits. JEL Classification: C13, C14, C41
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