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A simple nonparametric test for diagnosing nonlinearity in Tobit median regression model

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  • Wang, Lan

Abstract

In many applications, the response variable is observed only when it is above or below a given threshold otherwise the threshold itself is observed. Tobit median regression model is a useful semiparametric procedure for analyzing this type of censored data. We propose a simple nonparametric test for assessing the common linearity assumption in this model. Compared to those existing methods in the literature, the new test has the advantage of allowing the alternative to be any smooth function. In addition, it does not require any knowledge of the parametric distribution of the random error. The test is asymptotically normal under the null hypothesis of linearity. A small Monte Carlo study demonstrates its performance.

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  • Wang, Lan, 2007. "A simple nonparametric test for diagnosing nonlinearity in Tobit median regression model," Statistics & Probability Letters, Elsevier, vol. 77(10), pages 1034-1042, June.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:10:p:1034-1042
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    Cited by:

    1. Weichi Wu & Zhou Zhou, 2017. "Nonparametric Inference for Time-Varying Coefficient Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 98-109, January.
    2. Song, Weixing & Yao, Weixin, 2011. "A lack-of-fit test in Tobit errors-in-variables regression models," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1792-1801.
    3. 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.
    4. 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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