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Tobit model with covariate dependent thresholds


  • Omori, Yasuhiro
  • Miyawaki, Koji


Tobit models are extended to allow threshold values which depend on individuals' characteristics. In such models, the parameters are subject to as many inequality constraints as the number of observations, and the maximum likelihood estimation which requires the numerical maximisation of the likelihood is often difficult to be implemented. Using a Bayesian approach, a Gibbs sampler algorithm is proposed and, further, the convergence to the posterior distribution is accelerated by introducing an additional scale transformation step. The procedure is illustrated using the simulated data, wage data and prime rate changes' data.

Suggested Citation

  • Omori, Yasuhiro & Miyawaki, Koji, 2010. "Tobit model with covariate dependent thresholds," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2736-2752, November.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2736-2752

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    References listed on IDEAS

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    Cited by:

    1. Wichitaksorn, Nuttanan & Tsurumi, Hiroki, 2013. "Comparison of MCMC algorithms for the estimation of Tobit model with non-normal error: The case of asymmetric Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 226-235.
    2. Marra, Giampiero & Wyszynski, Karol, 2016. "Semi-parametric copula sample selection models for count responses," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 110-129.
    3. Marra, Giampiero & Radice, Rosalba, 2013. "Estimation of a regression spline sample selection model," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 158-173.
    4. Li, He & Zhang, Zhichao & Zhang, Chuanjie, 2017. "China’s intervention in the central parity rate: A Bayesian Tobit analysis," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 612-624.

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