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b. Heteroscedasticity Test
If heteroscedasticity is detected in a regression model, thus the standard error from regression can be refraction bias. As a
consequences, all of the hypothesis test can be mislead. According to Ariefinto 2012:39 heteroscedasticity problem causes the conclution
that concluded become invalid.
According to Fadhliyah 2008, there are several ways to detect the heteroscedasticity, it depends on the software used for conducting
the research. In eviews, white heteroscedasticity is used to test the heteroscedasticity.
Beside eviews, the other application named gretl can be used to detect heteroscedasticity through white heteroscedasticity test.
The following hypothesis is built for heteroscedasticity test: 1 H
: Heteroscedasticity is not present Homoscedastic 2 H
1
: Heteroscedastic According to Adkins 2011:173, the white test in gretl is similar
with the Breusch-Pagan test. The assumption to reject the null hypothesis is: if the p-
value α 0.05, thus H is rejected. Meanwhile,
if the p-value is α 0.05, thus H
is accepted.
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In gretl, the simplest way to tackle heteroscedasticity problem is to use least squares to estimate the intercept and slopes and use an
estimator of least squares covariance that is consistent. This is the so- called heteroscedasticity robust estimator of covariance Adkins,
2011:176. Meanwhile, in eviews, Heteroscedasticity can be eliminated
through White’s cross-section standard errors, if heteroscedasticity
caused by the cross section or White’s period standard errors, if heteroscedasticity caused by the variability over the time. If the
heteroscedasticity caused by both cross section and time series, thus the it can eliminate through Whi
te’s diagonal standard errors.
c. Multicollinearity Test
The independent variables which contain of multicollinearity make the coefficient of regression become unsuitable with the
substances, thus the interpretation become inappropriate Fadhliyah, 2008.
According to Wibowo, 2012: 87, one way to detect multicollinearity in SPSS is to use a test tools that called Variance
Inflation Factor VIF. According to Nachrowi and Usman 2006 in Fadhliyah 2008,
the strong multicollinearity has value 0.8.
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