Regresi Kuantil Median untuk Mengatasi Heteroskedastisitas pada Analisis Regresi

Ir. I Putu Eka Nila Kencana, MT, I Putu Eka Nila Kencana (2013) Regresi Kuantil Median untuk Mengatasi Heteroskedastisitas pada Analisis Regresi. E - Jurnal Matematika, 2 (1). ISSN 1693-1394

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In regression analysis, the method used to estimate the parameters is Ordinary Least Squares (OLS). The principle of OLS is to minimize the sum of squares error. If any of the assumptions were not met, the results of the OLS estimates are no longer best, linear, and unbiased estimator (BLUE). One of the assumptions that must be met is the assumption about homoscedasticity, a condition in which the variance of the error is constant (same). Violation of the assumptions about homoscedasticity is referred to heteroscedasticity. When there exists heteroscedasticity, other regression techniques are needed, such as median quantile regression which is done by defining the median as a solution to minimize sum of absolute error. This study intended to estimate the regression parameters of the data were known to have heteroscedasticity. The secondary data were taken from the book Basic Econometrics (Gujarati, 2004) and analyzing method were performed by EViews 6. Parameter estimation of the median quantile regression were done by estimating the regression parameters at each quantile Ï„th, then an estimator was chosen on the median quantile as regression coefficients estimator. The result showed heteroscedasticity problem has been solved with median quantile regression although error still does not follow normal distribution properties with a value of R2 about 71 percent. Therefore it can be concluded that median quantile regression can overcome heteroscedasticity but the data still abnormalities

Item Type: Article
Uncontrolled Keywords: Heteroscedasticity, Median Quantile Regression, EV
Subjects: L Education > L Education (General)
Divisions: Faculty of Law, Arts and Social Sciences > School of Education
Depositing User: Mr. Repository Admin
Date Deposited: 07 Jun 2016 21:58
Last Modified: 21 Jun 2016 05:58

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