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Faculty of Economics

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Harvey, A. C. and Liao, Y.

Dynamic Tobit Models

Econometrics and Statistics

Vol. 26 pp. 72-83 (2023)

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.

Keywords: Censored distributions, dynamic conditional score model, EGARCH models, logistic distribution, generalized t distribution

Author links: Andrew Harvey  

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