skip to content

Faculty of Economics

Journal Cover

Harvey, A. C. and Liao, Y.

Dynamic Tobit models

Econometrics and Statistics

(2021)

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 modelslogistic distribution, generalized t distribution

Author links: Andrew Harvey  

Publisher's Link: https://doi.org/10.1016/j.ecosta.2021.08.012



Papers and Publications



Recent Publications


Elliott, M., Golub, B. and Leduc, M. V. Supply Network Formation and Fragility American Economic Review [2022]

Bhattacharya, D. The Empirical Content of Binary Choice Models Econometrica [2021]

Corsetti, G., Crowley, M. A. and Han, L. Invoicing and the Dynamics of Pricing-to-Market: Evidence from UK Export Prices around the Brexit Referendum Journal of International Economics [2022]

Ding, Y. A Simple Joint Model for Returns, Volatility and Volatility of Volatility Journal of Econometrics [2023]