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Seemingly unrelated regressions with covariance matrix of cross-equation ridge regression residuals
Karolinska Institute.
Florida International University, USA.
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. (Economics and Statistics)ORCID iD: 0000-0002-3416-5896
2018 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, p. 1-25Article 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, 2018. p. 1-25
Keywords [en]
Multicollinearity, Ridge regression, Seemingly unrelated regressions
National Category
Probability Theory and Statistics
Research subject
Mathematics, Applied Mathematics
Identifiers
URN: urn:nbn:se:lnu:diva-67928DOI: 10.1080/03610926.2017.1383431OAI: oai:DiVA.org:lnu-67928DiVA, id: diva2:1140706
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-03-02

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Shukur, Ghazi

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