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


Merrick Li, Z. and Linton, O. A ReMeDI for Microstructure Noise Econometrica [2022]

Ambrus, A. and Elliott, M. Investments in Social Ties, Risk Sharing, and Inequality Review of Economic Studies [2021]

Li, S. and Linton, O. When Will the Covid-19 Pandemic Peak? Journal of Econometrics [2021]

Elliott, M., Georg, C-P. and Hazell, J. Systemic Risk Shifting in Financial Networks Journal of Economic Theory [2021]