PENDUGA PARAMETER MODEL REGRESI LINIER SEDERHANA HADIRNYA HETEROSKEDASITAS DAN PENCILAN DENGAN METODE ROBUST WILD BOOTSTRAP
DOI:
https://doi.org/10.24114/jmk.v7i3.32478Keywords:
Penduga parameter, Heteroskedasitas (Heteroskedastik), Pencilan (Outliers), Efesiensi, OLS, Bootstrap klasik, Robust Wild BootstrapAbstract
Ordinary Least Square (OLS) merupakan suatu metode yang biasanya digunakan untuk mengestimasi parameter sebuah model regresi linier. Namun, ketika suatu data memiliki heteroskedastisitas dan pencilan dalam model regresi akan menyebabkan metode OLS menghasilkan penduga parameter yang tidak efisien.Penelitian ini dilakukan bertujuan untuk menganalisis efesiensi penduga parameter regresi linier berganda hadirnya heteroskedastik dan pencilan dengan metode robust wild bootstrap. Metode robust wild bootstrap adalah salah satu metode yang digunakan untuk mengestimasi parameter model regresi dengan hadirnya heteroskedastik dan pencilan yang merupakan modifikasi dari metode wild bootstrap. Hasil analisis yang telah dilakukan menunjukkan bahwa metode robust wild bootstrap menghasilkan penduga parameter yang lebih efisien dibandingkan dengan penduga parameter OLS, yang mana nilai mean standard error pada metode robust wild bootstrap lebih kecil dibandingkan dengan metode OLS. Sehingga dapat disimpulkan bahwa metode ini merupakan metode yang sesuai untuk kemungkinan variansi residual yang heteroskedastik dan adanaya pencilan. Abstract- Ordinary Least Square (OLS) is a method that is usually used to estimate the parameters of a linear regression model. However, when a data has heteroscedasticity and outliers in the regression model, it will cause the OLS method to produce inefficient parameter estimators. The robust wild bootstrap method is one of the methods used to estimate the parameters of the regression model with the presence of heteroscedasticity and outliers which is a modification of the wild bootstrap method. The results of the analysis that have been carried out show that the robust wild bootstrap method produces more efficient parameter estimators than the OLS parameter estimator, in which the mean standard error of the robust wild bootstrap method is smaller than the OLS method. So it can be concluded that this method is an appropriate method for the possibility of heteroscedastic residual variance and the existence of outliersReferences
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