Estimasi Model Seemingly Unrelated Regression (SUR) dengan Metode Generalized Least Square (GLS)

  • Ade Widyaningsih Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
  • Made Susilawati Faculty of Mathematics and Natural Sciences, Udayana University
  • I Wayan Sumarjaya Faculty of Mathematics and Natural Sciences, Udayana University

Abstract

Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR) in which parameters are estimated using Generalized Least Square (GLS). In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.

Author Biographies

Ade Widyaningsih, Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Made Susilawati, Faculty of Mathematics and Natural Sciences, Udayana University

Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

I Wayan Sumarjaya, Faculty of Mathematics and Natural Sciences, Udayana University

Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Published
2014-06-30
How to Cite
WIDYANINGSIH, Ade; SUSILAWATI, Made; SUMARJAYA, I Wayan. Estimasi Model Seemingly Unrelated Regression (SUR) dengan Metode Generalized Least Square (GLS). Jurnal Matematika, [S.l.], v. 4, n. 2, p. 102 - 110, june 2014. ISSN 2655-0016. Available at: <https://ojs.unud.ac.id/index.php/jmat/article/view/12554>. Date accessed: 19 nov. 2019. doi: https://doi.org/10.24843/JMAT.2014.v04.i02.p49.
Section
Articles

Keywords

Multiple Linear Regression; Ordinary Least Square; Seemingly Unrelated Regression; Generalized Least Square

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