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Dynamic Tobit models

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  • Harvey, Andew
  • Liao, Yin

Abstract

Score-driven models provide a solution to the problem of modeling time series when the observations are subject to censoring and location and/or scale may change over time. The method applies to generalized t and EGB2 distributions, as well as to the normal distribution. Explanatory variables can be included, making static Tobit models a special case. A set of Monte Carlo experiments show that the score-driven model provides good forecasts even when the true model is parameter-driven. The viability of the new models is illustrated by fitting them to data on Chinese stock returns.

Suggested Citation

  • Harvey, Andew & Liao, Yin, 2023. "Dynamic Tobit models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 72-83.
    • Handle: RePEc:eee:ecosta:v:26:y:2023:i:c:p:72-83
    DOI: 10.1016/j.ecosta.2021.08.012
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    References listed on IDEAS

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    1. Chao Wang & Kung-Sik Chan, 2018. "Quasi-Likelihood Estimation of a Censored Autoregressive Model With Exogenous Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1135-1145, July.
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    Cited by:

    1. Zuoxiang Zhao & Hongjun Sun & Ding Han & Qiuyun Zhao, 2023. "Development strategy, technological progress, and regional environmental performance: empirical evidence from China," Economic Change and Restructuring, Springer, vol. 56(5), pages 3701-3732, October.

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