Maximum likelihood estimation of spatial lag models in the presence of the error-prone variables

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dc.authorid0000-0002-4630-2114en_US
dc.authorid0000-0002-4127-7108en_US
dc.authorid0000-0002-7335-8578en_US
dc.contributor.authorEralp, Anıl
dc.contributor.authorGökmen, Şahika
dc.contributor.authorDağalp, Rukiye
dc.date.accessioned2023-08-18T08:26:06Z
dc.date.available2023-08-18T08:26:06Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, İktisadi ve İdari Bilimler Fakültesi, Ekonomertri Bölümüen_US
dc.descriptionSahika Gokmen has been supported by TUBITAK 2219 Pot-doc scholarship.en_US
dc.description.abstractThe literature has recently devoted close attention to error-prone variables. Nevertheless, only a small number of research have considered measurement error in spatial econometric models. The presence of measurement error in the spatial econometric models needs to be considered as a result of the rise in spatial data analysis, as the relationship between the spatial correlation and measurement error influences parameter estimation. Therefore, in this study, the impacts of classical measurement error on the parameter estimation of the spatial lag model are theoretically examined for both response and explanatory variables. Then, using simulation studies, finite sample properties are investigated for various situations. The major findings indicate that although error-prone response variable has an opposing bias effect on parameter estimations, error-prone explanatory variables have a significant influence effect on the bias of parameter estimations. As a result, it is occasionally possible to obtain unbiased estimates only in certain circumstances.en_US
dc.description.sponsorshipTUBITAK 2219en_US
dc.identifier.citationEralp, A., Gokmen, S., & Dagalp, R. (2023). Maximum likelihood estimation of spatial lag models in the presence of the error-prone variables. Communications in Statistics-Theory and Methods, 52(10), 3229-3240.en_US
dc.identifier.doi10.1080/03610926.2022.2147795
dc.identifier.endpage3240en_US
dc.identifier.issn0361-0926
dc.identifier.issn1532-415X
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85144099249en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage3229en_US
dc.identifier.urihttp://dx.doi.org/10.1080/03610926.2022.2147795
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11573
dc.identifier.volume52en_US
dc.identifier.wosWOS:000894205100001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorEralp, Anıl
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofCommunications in Statistics-Theory and Methodsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.tubitakTUBITAK 2219
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSpatial Econometrics Modelsen_US
dc.subjectSpatial Lag Modelen_US
dc.subjectSpatial Autoregressive Modelen_US
dc.subjectError-Prone Variablesen_US
dc.subjectSimulation Studyen_US
dc.titleMaximum likelihood estimation of spatial lag models in the presence of the error-prone variablesen_US
dc.typeArticleen_US

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