Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.spl.2004.10.022
DC FieldValue
dc.titleEstimating parameters in autoregressive models with asymmetric innovations
dc.contributor.authorWong, W.-K.
dc.contributor.authorBian, G.
dc.date.accessioned2011-05-03T08:09:14Z
dc.date.available2011-05-03T08:09:14Z
dc.date.issued2005
dc.identifier.citationWong, W.-K., Bian, G. (2005). Estimating parameters in autoregressive models with asymmetric innovations. Statistics and Probability Letters 71 (1) : 61-70. ScholarBank@NUS Repository. https://doi.org/10.1016/j.spl.2004.10.022
dc.identifier.issn01677152
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/22375
dc.description.abstractTiku et al. (Theory Methods 28(2) (1999) 315) considered the estimation in a regression model with autocorrelated error in which the underlying distribution be a shift-scaled Student's t distribution, developed the modified maximum likelihood (MML) estimators of the parameters and showed that the proposed estimators had closed forms and were remarkably efficient and robust. In this paper, we extend the results to the case, where the underlying distribution is a generalized logistic distribution. The generalized logistic distribution family represents very wide skew distributions ranging from highly right skewed to highly left skewed. Analogously, we develop the MML estimators since the ML (maximum likelihood) estimators are intractable for the generalized logistic data. We then study the asymptotic properties of the proposed estimators and conduct simulation to the study. © 2004 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.spl.2004.10.022
dc.sourceScopus
dc.subjectAutoregression
dc.subjectGeneralized logistic distribution
dc.subjectLeast squares
dc.subjectModified maximum likelihood
dc.subjectNonnormality
dc.subjectRobustness
dc.typeArticle
dc.contributor.departmentECONOMICS
dc.description.doi10.1016/j.spl.2004.10.022
dc.description.sourcetitleStatistics and Probability Letters
dc.description.volume71
dc.description.issue1
dc.description.page61-70
dc.description.codenSPLTD
dc.identifier.isiut000226338300007
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

17
checked on Jan 17, 2020

WEB OF SCIENCETM
Citations

19
checked on Jan 10, 2020

Page view(s)

205
checked on Dec 31, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.