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Seemingly unrelated regressions with covariance matrix of cross-equation ridge regression residuals
Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden.ORCID iD: 0000-0003-2733-4441
Department of Mathematics and Statistics, Florida International University, Miami, USA.
Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Economics and Statistics, Linnaeus University, Växjö, Sweden.ORCID iD: 0000-0002-3416-5896
2017 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Generalized least squares estimation of a system of seemingly unrelated regressions is usually a two-stage method: (1) estimation of cross-equation covariance matrix from ordinary least squares residuals for transforming data, and (2) application of least squares on transformed data. In presence of multicollinearity problem, conventionally ridge regression is applied at stage 2. We investigate the usage of ridge residuals at stage 1, and show analytically that the covariance matrix based on the least squares residuals does not always result in more efficient estimator. A simulation study and an application to a system of firms' gross investment support our finding.

Place, publisher, year, edition, pages
Taylor & Francis, 2017.
Keywords [en]
Multicollinearity, Ridge regression, Seemingly unrelated regressions
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-37275DOI: 10.1080/03610926.2017.1383431ISI: XYZScopus ID: 2-s2.0-85033673015Local ID: IHHÖvrigtISOAI: oai:DiVA.org:hj-37275DiVA, id: diva2:1140708
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-06-07

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Zeebari, ZanginShukur, Ghazi

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