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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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d. Autocorrelation Test
According to Ariefianto 2012:30 the commonly uses testing method to test the autocorrelation is through Durbin-Watson test DW
tests. The decision making based on the Durbin-Watson test can be categorized into:
1 4 – d
1
DW 4 ; indicates the negative autocorrelation 2 4
– d
u
DW 4 – dl ; indicates the indeterminate
3 2 DW 4 – d
u
; indicates that there is no autocorrelation 4 d
1
dw d
u
; indicates the indeterminate 5 0 DW d
L
; indicates the postive autocorrelation The value of du and dl acquired from Durbin Watson statistic
table. According to Gujarati 2004:475, if a research is using Generalized
Least Square GLS model, then a model contains of autocorrelation problem in a panel data, thus, the output will be free from
autocorrelation problem.
4. Panel Data Regression
Panel data also known as longitudinal or cross-sectional time-series data is a dataset in which the behavior of entities is observed cross
section across time. Table 3.1 will show the difference between cross sectional and time series data
