Abstract
In the regression analysis we need a method to estimate parameters to fulfill the BLUE characteristic. There are assumptions that must be fulfilled homoscedasticity one of which is a condition in which the assumption of error variance is constant (same), infraction from the assumptions homoskedasticity called heteroscedasticity. The Consequence of going heteroscedasticity can impact OLS estimators still fulfill the requirements of not biased, but the variant obtained becomes inefficient. So we need a method to solve these problems either by Weighted Least Square (WLS). The purpose of this study is to find out how to overcome heteroscedasticity in regression with WLS. Step of this research was do with the OLS analysis, then testing to see whether there heteroscedasticity problem with BPG method, the next step is to repair the beginning model by way of weighting the data an exact multiplier factor, then re-using the OLS procedure to the data that have been weighted, the last stage is back with a heteroscedasticity test BPG method, so we obtained the model fulfill the assumptions of homoskedasicity. Estimates indicate that the WLS method can resolve the heteroscedasticity, with exact weighting factors based on the distribution pattern of data seen.
Highlights
In the regression analysis we need a method to estimate parameters to fulfill the Best Linear Unbiased Estimator (BLUE) characteristic
The purpose of this study is to find out how to overcome heteroscedasticity in regression with Weighted Least Square (WLS)
Regresi Kuantil Median Untuk Mengatasi Heteroskedastisitas Pada Analisis Regresi
Summary
Dalam analisis regresi diperlukan suatu metode untuk menduga parameter agar memenuhi sifat BLUE Estimator), salah satu metode yang paling sering digunakan adalah Ordinary Least Square (OLS). Salah satu asumsi klasik yang harus dipenuhi dalam estimasi OLS agar hasil estimasinya dapat diandalkan, yaitu ragam sisaan homogen ( ). Pelanggaran terhadap asumsi homoskedastisitas disebut heteroskedastisitas, yang artinya galat bersifat tidak konstan. Metode Weighted Least Square (WLS) merupakan metode alternatif yang dapat mengatasi heteroskedastisitas. Karena pada OLS diasumsikan bahwa nilai duga parameter regresi bernilai sama untuk setiap observasi. Mekanisme kerja WLS yaitu apabila varian variabel gangguan tidak diketahui maka untuk menyelesaikan masalah heteroskedastisitas adalah dengan mengetahui pola heteroskedastisitas itu sendiri, jika variabel gangguan proporsional terhadap maka model akan dibagi dengan √ , jika varian variabel gangguan adalah proporsional terhadap sehingga model akan dibagi dengan , selain proporsional dengan dan bisa juga diasumsikan bahwa pola varian variabel gangguan adalah proporsional terhadap [ ].
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