Dynamic Tobit models
Andrew Harvey and
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Score-driven models provide a solution to the problem of modelling 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. A set of Monte Carlo experiments show that the score-driven model provides good forecasts even when the true model is parameterdriven. 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 (search for similar items in EconPapers)
JEL-codes: C22 C24 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:1913
Access Statistics for this paper
More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Bibliographic data for series maintained by Jake Dyer ().