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Heteroscedastic Transformation Models With Covariate Dependent Censoring

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  • Khan, Shakeeb
  • Shin, Youngki
  • Tamer, Elie

Abstract

In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity 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 an estimator and show that it is √ n -consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We also use the estimator in an empirical illustration of export duration, where we find advantages of the proposed method over existing ones.
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  • Khan, Shakeeb & Shin, Youngki & Tamer, Elie, 2011. "Heteroscedastic Transformation Models With Covariate Dependent Censoring," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 40-48.
    • Handle: RePEc:bes:jnlbes:v:29:i:1:y:2011:p:40-48
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    Cited by:

    1. Kloodt, Nick, 2021. "Identification in a fully nonparametric transformation model with heteroscedasticity," Statistics & Probability Letters, Elsevier, vol. 170(C).
    2. Yao Luo & Isabelle Perrigne & Quang Vuong, 2018. "Structural Analysis of Nonlinear Pricing," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2523-2568.

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