MENGATASI HETEROSKEDASTISITAS PADA REGRESI DENGAN MENGGUNAKAN WEIGHTED LEAST SQUARE
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.
Penulis: PUTU AYU MAZIYYA, I
KOMANG GDE SUKARSA, NI MADE ASIH
Kode Jurnal: jpmatematikadd150094
