Performances of Ordinary and Generalized Least Squares Estimators on Multiple Linear Regression Models with Heteroscedasticity

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dc.contributor.authorAdejumo, A.O.
dc.contributor.authorJob, O.
dc.contributor.authorIsaac, T.D.
dc.contributor.authorOyejola, B.A.
dc.date.accessioned2018-12-18T09:27:24Z
dc.date.available2018-12-18T09:27:24Z
dc.date.issued2016-05
dc.description.abstractThis paper focuses on the impact of heteroscedasticity on the estimate and variance of model parameters by studying the performances of Ordinary Least Square (OLS) and General Least Square (GLS) estimators in multiple linear regression models with two independent variables only and error term characterized with different magnitudes of heteroscedasticity related to predictors at different sample sizes. This research explored the patterns of the variance estimates offered by the two estimators when data is front with heteroscedasticity. Also the studies explored the situations where concepts of substitute, complementary or joint demands/supply may reflect in the stochastic characterization of the error term. From Monte Carlo simulation studies using R-package the GLS estimator maintains its superiority over the OLS in multiple linear regression models.en_US
dc.description.sponsorshipSelfen_US
dc.identifier.urihttp://hdl.handle.net/123456789/1427
dc.language.isoenen_US
dc.publisherAkama Universityen_US
dc.relation.ispartofseriesThe Pacific Journal of Science and Technology;17(1), 68-78
dc.subjectheteroscedasticityen_US
dc.subjectordinary least squaresen_US
dc.subjectgeneralized least squaresen_US
dc.subjectstochastic error termen_US
dc.subjectmagnitudeen_US
dc.subjectMonte Carloen_US
dc.subjectsimulationen_US
dc.subjectcharacterizationen_US
dc.titlePerformances of Ordinary and Generalized Least Squares Estimators on Multiple Linear Regression Models with Heteroscedasticityen_US
dc.typeArticleen_US

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