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http://hdl.handle.net/20.500.12358/24181
TitleExplicit Equations to Determine the Variances of Regression Coefficients of OLS and GLS Estimators In An Auto-Correlated Regression Models
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Abstract

We have derived explicit equations to determine the variances of the regression coefficients of ordinary least squares (OLS) and generalized least squares (GLS) estimators in regression models containing an auto-correlated disturbance term for any covariance matrix and design vectors. In addition, we have proved that scaling or shifting the design vector has no effect in the relative efficiency of the variance of GLS to that of OLS. Keywords: Variance; Ordinary Least Squares; Generalized Least Squares; Efficiency

Authors
Safi, Samir K.
TypeJournal Article
Date2008
LanguageEnglish
Subjects
neural networks
forecasting
box-jenkins methodology
multilayer perceptrons
Published inIUG Journal for Natural and Engineering Studies
SeriesVolume: 16, Number: 1
Publisherالجامعة الإسلامية - غزة
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  • Staff Publications- Faculty of Economics and Administrative Sciences [292]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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