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 (). This e-mail address is bad, please contact .