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Semiparametric estimation of a bivariate Tobit model

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  • Chen, Songnian
  • Zhou, Xianbo

Abstract

The existing semiparametric estimation literature has mainly focused on univariate Tobit models and no semiparametric estimation has been considered for bivariate Tobit models. In this paper, we consider semiparametric estimation of the bivariate Tobit model proposed by Amemiya (1974), under the independence condition without imposing any parametric restriction on the error distribution. Our estimator is shown to be consistent and asymptotically normal, and simulation results show that our estimator performs well in finite samples. It is also worth noting that while Amemiya’s (1974) instrumental variables estimator (IV) requires the normality assumption, our semiparametric estimator actually outperforms his IV estimator even when normality holds. Our approach can be extended to higher dimensional multivariate Tobit models.

Suggested Citation

  • Chen, Songnian & Zhou, Xianbo, 2011. "Semiparametric estimation of a bivariate Tobit model," Journal of Econometrics, Elsevier, vol. 165(2), pages 266-274.
    • Handle: RePEc:eee:econom:v:165:y:2011:i:2:p:266-274
    DOI: 10.1016/j.jeconom.2011.07.005
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    Cited by:

    1. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.
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    3. F. Louzada & P. H. Ferreira, 2016. "Modified inference function for margins for the bivariate clayton copula-based SUN Tobit Model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(16), pages 2956-2976, December.

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