
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: https://doi.org/10.1016/j.ecosta.2021.08.012