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Empirical L2-distance lack-of-fit tests for Tobit regression models

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  • Song, Weixing
  • Zhang, Yi

Abstract

Standard Tobit regression models assume a linear relationship between the partially observed response variable and the predictors, while applications often see some nonlinear connections. This paper proposes an empirical L2-distance lack-of-fit test to check the adequacy of the presumed parametric form for the regression function in Tobit regression models. The proposed test statistic is shown to be asymptotically normal, consistent against some fixed alternatives, and has nontrivial power for some local nonparametric alternatives. Simulation studies are conducted to assess the finite sample performance of the proposed test.

Suggested Citation

  • Song, Weixing & Zhang, Yi, 2012. "Empirical L2-distance lack-of-fit tests for Tobit regression models," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 380-396.
    • Handle: RePEc:eee:jmvana:v:111:y:2012:i:c:p:380-396
    DOI: 10.1016/j.jmva.2012.04.015
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    References listed on IDEAS

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    Cited by:

    1. Koul, Hira L. & Song, Weixing & Liu, Shan, 2014. "Model checking in Tobit regression via nonparametric smoothing," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 36-49.

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