2014
DOI: 10.25077/jmu.3.4.168-176.2014
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Perbandingan Penduga Ordinary Least Squares (Ols) Dan Generalized Least Squares (Gls) Pada Model Regresi Linier Dengan Regresor Bersifat Stokastik Dan Galat Model Berautokorelasi

Abstract: Pendugaan parameter model regresi linier pada analisis regresi linier, biasanyadilakukan dengan metode penduga OLS. Penduga OLS harus memenuhi asumsi-asumsistatistik yang disebut dengan asumsi klasik. Jika asumsi tidak dipenuhi, maka akanmenghasilkan kesimpulan yang tidak valid sehingga penduga OLS tidak bisa digunakanlagi dalam melakukan pendugaan parameter. Oleh karena itu diperlukan metode pendugaan lain untuk memperoleh hasil yang valid yaitu penduga GLS. Pelanggaran asumsidiantaranya terdapat autokorelasi… Show more

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“…After carrying out various stages to get the best model for panel data regression, it was found that the Common Effect Model with the Generalized Least Square (GLS) method and the cross-section Seemingly Unrelated Regression (SUR) was the best model. CEM by weighing the covariance coefficient of cross-section SUR is more efficient than the OLS method for estimating data with autoclave residuals (Iswati et al, 2014). Table 3 shows the estimation results used in this study are the Common Effect Model with the Generalized Least Square (GLS) method and the cross-section Seemingly Unrelated Regression (SUR).…”
Section: Resultsmentioning
confidence: 99%
“…After carrying out various stages to get the best model for panel data regression, it was found that the Common Effect Model with the Generalized Least Square (GLS) method and the cross-section Seemingly Unrelated Regression (SUR) was the best model. CEM by weighing the covariance coefficient of cross-section SUR is more efficient than the OLS method for estimating data with autoclave residuals (Iswati et al, 2014). Table 3 shows the estimation results used in this study are the Common Effect Model with the Generalized Least Square (GLS) method and the cross-section Seemingly Unrelated Regression (SUR).…”
Section: Resultsmentioning
confidence: 99%
“…Repair of classical assumptions for this model is carried out using the Generalized Least Square (GLS) method. GLS efficiently estimates data with model errors autocorrelation (GLS) (Iswati, Syahni, & Maiyastri, 2014). Based on Table 3, the results of the regression equation are as follows:…”
Section: Data Estimation Resultsmentioning
confidence: 99%
“…Therefore, the regression test was carried out using the Generalized Least Squares (GLS) model. GLS produces more stable and efficient estimates than OLS (Iswati et al, 2014) due to its ability to overcome the problems of time series and cross-sectional autocorrelation and violation of homoscedastic assumptions (Kosmaryati et al, 2019).…”
Section: Methodsmentioning
confidence: 99%