Fusion of data sets in multivariate linear regression with errors-in-variables

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• dc.contributor.author Satorra, Albertca
• dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
• dc.date.accessioned 2017-07-26T12:07:57Z
• dc.date.available 2017-07-26T12:07:57Z
• dc.date.issued 1996-10-01
• dc.date.modified 2017-07-23T02:02:42Z
• dc.description.abstract We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regression models with errors--in--variables, in the case where various data sets are merged into a single analysis and the observable variables deviate possibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possible non--normality of the data, normal--theory methods yield correct inferences for the parameters of interest and for the goodness--of--fit test. The theory described encompasses both the functional and structural model cases, and can be implemented using standard software for structural equations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.
• dc.format.mimetype application/pdfca
• dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=183
• dc.identifier.citation Classification and Knowledge Organization, (1997), pp. 195-207
• dc.identifier.uri http://hdl.handle.net/10230/1235
• dc.language.iso eng
• dc.relation.ispartofseries Economics and Business Working Papers Series; 183
• dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
• dc.subject.keyword asymptotic robustness
• dc.subject.keyword multivariate regression
• dc.subject.keyword asymptotic efficiency
• dc.subject.keyword normal theory methods
• dc.subject.keyword multi--samples
• dc.subject.keyword errors--in--variables
• dc.subject.keyword Statistics, Econometrics and Quantitative Methods
• dc.title Fusion of data sets in multivariate linear regression with errors-in-variablesca
• dc.type info:eu-repo/semantics/workingPaper