Comparison of several estimators for the covariance of the coefficient matrix

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buir.advisorZaman, Asad
dc.contributor.authorOrhan, Mehmet
dc.date.accessioned2016-01-08T20:07:40Z
dc.date.available2016-01-08T20:07:40Z
dc.date.issued1995
dc.descriptionAnkara : The Department of Economics and the Institute of Economics and Social Sciences of Bilkent Univ., 1995.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 1995.en_US
dc.descriptionIncludes bibliographical references leaves 41-42.en_US
dc.description.abstractThe standard regression analysis assumes that the variances of the disturbance terms are constant, and the ordinary least squares (OLS) method employs this very crucial assumption to estimate the covariance of the disturbance terms perfectly, but OLS fails to estimate well when the variance of the disturbance terms vary across the observations. A very good method suggested by Eicker and improved by White to estimate the covariance matrix of the disturbance terms in case of heteroskedeisticity was proved to be biased. This paper evaluates the performance of White’s method as well as the OLS method in several different settings of regression. Furthermore, bootstrapping, a new method which very heavily depends on computer simulation is included. Several types of this method are used in several cases of homoskedastic, heteroskedastic, balanced, and unbalanced regressions.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T20:07:40Z (GMT). No. of bitstreams: 1 0008042.pdf: 1101438 bytes, checksum: 8f6b5ba4522048d1c9702b7cec335ef0 (MD5)en
dc.description.statementofresponsibilityOrhan, Mehmeten_US
dc.format.extentxii, 42 leaves, graphicsen_US
dc.identifier.urihttp://hdl.handle.net/11693/17160
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHomoskedasticityen_US
dc.subjectheteroskedasticityen_US
dc.subjectbalanced and unbalanced regressionen_US
dc.subjectbootstrappingen_US
dc.subjectOLSen_US
dc.subject.lccHA31.35 .O74 1995en_US
dc.subject.lcshAnalysis of variance.en_US
dc.subject.lcshRegression analysis.en_US
dc.subject.lcshMatrices.en_US
dc.subject.lcshMathematical statistics.en_US
dc.subject.lcshEconometric models.en_US
dc.subject.lcshEconomics--Mathematical models.en_US
dc.subject.lcshSocial sciences--Statistical methods.en_US
dc.titleComparison of several estimators for the covariance of the coefficient matrixen_US
dc.typeThesisen_US
thesis.degree.disciplineEconomics
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMA (Master of Arts)

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