Estimasi Model Seemingly Unrelated Regression (SUR) dengan Metode Generalized Least Square (GLS)
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.
Keywords: Multiple Linear
Regression; Ordinary Least Square; Seemingly Unrelated Regression; Generalized
Least Square
Penulis: Ade Widyaningsih,
Made Susilawati, I Wayan Sumarjaya
Kode Jurnal: jpmatematikadd141240
