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Explanation of Table 4.1: 1. The dependent variable of the stocks rate of return in consumer goods
sector has an average return of 0.022268. The maximum return of consumer goods sector during the period of observation is 0.193360, while
the minimum return is -0.169444 with the 6.785 standard deviation. In the meantime, the dependent variable of the stocks rate of return in
property and real estate sector has an average return of 0.019215. The maximum return of property and real estate sector during the period of
observation is 0.306532, while the minimum return is -0.340247with the 9.9661 standard deviation.
2. As for the independent variable, the change in exchange rate during the period of the observation has an average change of -0.000831. The
maximum change is 0.172425 and the minimum change is -0.098840 with 3.7145 standard deviation. Another variable, which is change in inflation
rate, has an average change of -0.483316. The maximum change is 9.000000 and the minimum change is -35.00000with 519.2304 standard
deviation. And as the last independent variable, which is change in SBI rate, it has the average change of -0.011012. The maximum change is
0.130793 and the minimum change is -0.105425 with 3.6939 standard deviation.
C. Normality Test
In this research, the writer uses the normality test of Jarque Bera using statistical software Eviews 5. Normality test is used to test whether the residual of the model
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used in the research is normally distributed or not. The diagram below present the result of normality test using histogram residualJarque-Bera method for the
consumer goods sector:
Figure 4.1 Histogram Residual, Consumer Goods Sector
Figure 4.2 Histogram Residual, Property and Real Estate Sector
1 2
3 4
5 6
7 8
9
-0.10 -0.05
-0.00 0.05
0.10 0.15
Series: Residuals Sample 2006M01 2010M12
Observations 60 Mean
-4.97e-18 Median
-0.005805 Maximum
0.145997 Minimum
-0.105790 Std. Dev.
0.057312 Skewness
0.487487 Kurtosis
2.684504 Jarque-Bera
2.625284 Probability
0.269108
2 4
6 8
10 12
14
-0.1 -0.0
0.1 0.2
Series: Residuals Sample 2006M01 2010M12
Observations 60
Mean 6.94e-19
Median 0.001880
Maximum 0.238803
Minimum -0.167392
Std. Dev. 0.079104
Skewness 0.363828
Kurtosis 3.382237
Jarque-Bera 1.688972
Probability 0.429778
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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
R-Squared Y
R-Squared X
1
R-Squared X
2
R-Squared X
3
Consumer Goods
0.286679 0.170209
0.039652 0.142669
Property and Real Estate
0.369993 0.170209
0.039652 0.142669
Table 4.6 Multi-Collinearity Test
When examining the existence of multi-collinearity, one should look at the partial correlation coefficient Gujarati, 2004. The table above describes the
summary of the linear regression, given each variable to be the dependent variable. For the consumer goods sector, the R-Squared of the Dependent Variable
Y is bigger than its independent variables y=28.6667 x
1
=17.0209 x
2
=3.9652 x
3
=14.26, and thus we reject H and assume that there is no
multi-collinearity existed in the data. The same thing goes for Property and Real Estate Sector, where the obtained R-squared Y is 36.99, which is bigger than its
independent variables. And thus, it is confirmed that multi- collinearity doesn‟t
exists as in the property and real estate as well.
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E. Multiple Linear Regression Model
Several classical assumption tests have done through the model to ensure that the model is a good estimator. In this research, the writer use the multiple
linear regression model to estimate the influence of the inflation rate, exchange rate and SBI rate to the return of stocks in consumer goods and property and real
estate sector. In order to achieve that, the writers use the statistical software Eviews 5 which is
resulted in models as follow:
4.1 Model of Consumer Goods Sector
4.2 Model of Property and Real Estate Sector
F. Hypothesis Testing 1. T-Test
