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From the Figure 4.1 we can see that the probability of Jarque Bera of the consumer goods sector is bigger than
α 0.269108 0.05. Thus, we failed to reject the Null Hypothesis and that means the residual of the model is normally
distributed. From the Figure 4.2 we can see that the probability of Jarque Bera of the property
and real estate sector is bigger than α 0.429778 0.05. Thus, we failed to reject
the Null Hypothesis and that means the residual of the model is normally distributed.
D. Classical Assumption Test
In order to achieve the model that has BLUE characteristic, the writer needs to conduct the classical assumption test before doing the regression
analysis. If in this test we find that the model is not resulting in BLUE characteristic we will use the remedial measurement accordingly.
1. Heteroscedasticity Test
One of the important assumptions of the classical linear regression model is that the variance of each disturbance, conditional on the chosen values of the
explanatory variables, is somewhat constant. This is the assumption of homoscedasticity.
To detect whether heteroscedasticity is present in the data, the writer will conduct a formal test using White Test method. The reason why we use White‟s General
Heteroscedasticity Test is because it does not rely on the normality assumption and easy to implement.
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After using the statistical software Eviews 5, the writer find the result of White Heteroscedasticity Test- No cross terms, as follows:
White Heteroskedasticity Test:
F-statistic 1.985143
Probability 0.084204
ObsR-squared 11.00974
Probability 0.088076
Table 4.2 Heteroscedasticity Test for Consumer Goods Sector
Table 4.2 presents the result of the White Heteroscedasticity test on consumer goods sector using statistical software Eviews 5. The result of the test provides the
proof that the data we are using is free from heteroscedastic problem homoscedastic. From the test above, we can see that the probability of ObsR-
squared is bigger than our significant value 0.088076 0.05 and thus we can reject the null hypothesis.
White Heteroskedasticity Test:
F-statistic 0.538939
Probability 0.776195
ObsR-squared 3.450216
Probability 0.750581
Table 4.3 Heteroscedasticity Test for Property and Real Estate Sector
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Table 4.3 presents the result of the White Heteroscedasticity test on property and real estate sector using statistical software Eviews 5. The result of the test
provides the proof that the data we are using is free from heteroscedastic problem homoscedastic. From the test above, we can see that the probability of ObsR-
squared is bigger than our significant value 0750581 0.05 and thus we can reject the null hypothesis.
2. Auto-Correlation Test
As in the case of heteroscedasticity, in the presence of autocorrelation the OLS estimators are still linear unbiased as well as consistent and asymptotically
normally distributed, but they are no longer efficient i.e., minimum variance.
To detect autocorrelation, the writer use The Breusch-Godfrey BG Test, which
is also known as Lagrange-Multiplier LM Test in the application software EViews 5. The results are presented in tables below:
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.150862
Probability 0.860328
ObsR-squared 0.333385
Probability 0.846460
Table 4.4 Auto-Correlation Test for Consumer Goods Sector
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.587759
Probability 0.559086
ObsR-squared 1.278303
Probability 0.527740
Table 4.5 Auto-Correlation Test for Property and Real Estate Sector
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From the Table 4.4 we find that the probability ObsR-squared from the data of consumer goods sector is bigger than sig. value
α 0.846460 0.05, thus we can conclude that auto-correlation is not existed in our data. The same result happens
in the property and real estate sector. As if presented in Table 4.5, the probability ObsR-squared is bigger than sig. value
α 0.527740 0.05, thus we can reject the null hypothesis and conclude that there is no auto-correlation in our data.
3. Multi-Collinearity Test
