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dc.contributor.authorDalla Valle, Luciana
dc.date.accessioned2016-11-25T15:15:47Z
dc.date.accessioned2017-03-28T14:29:47Z
dc.date.available2016-11-25T15:15:47Z
dc.date.available2017-03-28T14:29:47Z
dc.date.issued2016-11-23
dc.identifier.issn0282-423X
dc.identifier.issn2001-7367
dc.identifier.urihttp://hdl.handle.net/10026.1/8714
dc.description.abstract

<jats:title>Abstract</jats:title> <jats:p> Official statistics are a fundamental source of publicly available information that periodically provides a great amount of data on all major areas of citizens’ lives, such as economics, social development, education, and the environment. However, these extraordinary sources of information are often neglected, especially by business and industrial statisticians. In particular, data collected from small businesses, like small and medium-sized enterprizes (SMEs), are rarely integrated with official statistics data.</jats:p> <jats:p>In official statistics data integration, the quality of data is essential to guarantee reliable results. Considering the analysis of surveys on SMEs, one of the most common issues related to data quality is the high proportion of nonresponses that leads to self-selection bias.</jats:p> <jats:p>This work illustrates a flexible methodology to deal with self-selection bias, based on the generalization of Heckman’s two-step method with the introduction of copulas. This approach allows us to assume different distributions for the marginals and to express various dependence structures. The methodology is illustrated through a real data application, where the parameters are estimated according to the Bayesian approach and official statistics data are incorporated into the model via informative priors.</jats:p>

dc.format.extent887-905
dc.languageen
dc.language.isoen
dc.publisherWalter de Gruyter GmbH
dc.relation.replaceshttp://hdl.handle.net/10026.1/8029
dc.relation.replaces10026.1/8029
dc.subjectBayes theorem
dc.subjectcopulas
dc.subjectHeckman's two-step method
dc.subjectinformative priors
dc.subjectsmall and medium-sized enterprizes
dc.titleThe Use of Official Statistics in Self-Selection Bias Modeling
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000389674100007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue4
plymouth.volume32
plymouth.publication-statusPublished
plymouth.journalJournal of Official Statistics
dc.identifier.doi10.1515/jos-2016-0046
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2016-03-01
dc.identifier.eissn2001-7367
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1515/jos-2016-0046
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2016-11-23
rioxxterms.typeJournal Article/Review
plymouth.oa-locationhttps://www.degruyter.com/downloadpdf/j/jos.2016.32.issue-4/jos-2016-0046/jos-2016-0046.xml


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