skip to content

Faculty of Economics

Journal Cover

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  

Publisher's Link:

Papers and Publications

Recent Publications

Huffman, D., Raymond, C. and Shvets, J. Persistent Overconfidence and Biased Memory: Evidence from Managers American Economic Review [2022]

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

Toxvaerd, F.M.O. and Rowthorn, R. On the Management of Population Immunity Journal of Economic Theory [2022]

Mueller, H. and Rauh, C. The Hard Problem of Prediction for Conflict Prevention Journal of the European Economic Association [2022]