Developing a General Consistent Standard Error Estimator under Varying Strengths of Heteroscedasticity

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dc.contributor.authorJob, O.
dc.contributor.authorOyejola, B. A.
dc.date.accessioned2018-12-19T11:36:43Z
dc.date.available2018-12-19T11:36:43Z
dc.date.issued2015
dc.description.abstractIn econometric studies involving cross-sectional data, the assumption of a constant variance for the disturbance term is a fluke. In consumer budget studies (micro consumption function), the residual variance about the population regression function is very likely to increase with income. Also, in cross-sectional studies of firms the residual variance probably increases with the size of the firm. In a simple linear regression model, the dependent variable Y is explained by Z. Thus we assume y=f(z)+e postulate that . Let . By implication we formulate the assumption about in a rational and fairly general manner. In general, to validate this assumption, it is convenient and quite plausible to specify the form of association , where g is the strength of heteroscedasticity and the lower the strength (magnitude) of g, the smaller the difference between the individual variances. Except when g =0, the model is homoscedastic otherwise generally. This paper developed a general heteroscedasticity consistent standard error (HCSE) estimator using weight related to regressors that characterizes the random error term denoted by HC5. Comparative studies of the developed estimator with the existing HCSE estimators using various strengths of heteroscedasticity on a continuum scale at sample sizes 25, 30, 35, 40, 45, and 50 were implemented. The OLS estimator remains unbiased and the results showed that the developed estimator is indeed a generalization of all the existing HCSE estimators and proved to be consistent and asymptotically efficienten_US
dc.description.sponsorshipSelfen_US
dc.identifier.issn2408-4840
dc.identifier.urihttp://hdl.handle.net/123456789/1476
dc.language.isoenen_US
dc.publisherFaculty of Physical Sciences, University of Ilorinen_US
dc.relation.ispartofseriesIlorin Journal of Science;2(2), 262-284
dc.subjectLinearen_US
dc.subjectRegression Modelen_US
dc.subjectHeteroscedasticity Consistent Standard Error Estimatoren_US
dc.subjectMonte Carlo Simulationen_US
dc.subjectError termsen_US
dc.subjectmagnitudeen_US
dc.subjectweighting factoren_US
dc.subjectGeneralizationen_US
dc.titleDeveloping a General Consistent Standard Error Estimator under Varying Strengths of Heteroscedasticityen_US
dc.typeArticleen_US

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