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does not occur multicolinearity. While homogeneity
test of variance is used statistic of Breusch Pagan
(BP) and obtained value of BP = 7,8908>
(0,05;3)
= 7,815 so it is concluded that model there is
heteroscedasticity. In addition, spatial correlation
testing is also done between the errors by using
Moran Index. The test results showed positive spatial
autocorrelation with IM value = 0.24 which means
there is similarity error value from adjacent locations
and error value tend to group.
4.1 SEM Model
There is an indication of the spatial effect of the
Moran Index so that an analysis to test for the effect
is necessary. This test uses Lagrange Multiplier (LM)
lag and LM error statistics. LM lag statistic value of
2.1087 and LM error value of 4,0997 compared with
the value of chi square table of 3.851. The conclusion
obtained shows that there is no spatial effect of lag
but there is a spatial effect of error on the model so
that the appropriate model is the SEM model.
Determination of SEM model parameter
estimation with weighted queen obtained result with
each parameter is significant is as follows.
.
To overcome the non-homogeneous variance,
noise is added to the dependent variable. The noise is
generated from the normal distribution having a mean
of zero and the standard deviation is the standard
deviation error of 2.53. Noise is simulated 100 times.
The next step is modeled into the SEM model for each
noise simulation result and a spatial regression model
of the ensemble is obtained. The spatial model of the
ensemble model is the result of the average parameter
estimation p of the regression model, where p is the
number of spatial regression models. The spatial
regression ensemble model is expressed as
From the data analysis obtained by regression
model of spatial error ensemble from mean of
estimation result of hundredth parameter of model is
with the R
2
value of 0.7271, which means that 72% of
the total poor is affected by the percentage of
households using their own latrines / joints, the
percentage of households who have bought raskin
rice, and the percentage of population growth rate. To
see the feasibility of the model, tested normality with
statistics Kolmogorov Smirnov and obtained the test
statistic value is 0,147. With a significance level of 5
percent then taken the conclusion of the assumption
of normality error is met. Furthermore, homogeneity
test of variance is used statistic of Breusch Pagan
(BP) and obtained value of BP = 6:99240<
(0,05;3)
= 7,815 so it is concluded that the model there isn’t
heteroscedasticity again.
While the panel data analysis is the first done by
analysis with regular regression model. The
formation of the linear regression model is begun by
variable selection which is significant to the model
with stepwise method. In the case of poverty in
Central Java Province in 2008 until 2015, the
obtained linear regression model is
RMSE = 3.151822
To see the feasibility of the model, tested normality
with statistic Kolmogorov Smirnov and obtained the
test statistic value is 0.2071 > D(0.05;280) = 0.0807.
With a significance level of 5 percent then taken the
conclusion of the assumption of normality error is
met. Furthermore, for multicollinearity test with VIF,
from the four independent variables of the model
above shows the VIF value of each less than 10 which
it means that the model does not occur
multicollinearity. While homogeneity test of variance
used statistic Breusch Pagan (BP) and obtained value
of BP = 17.0267 >
so
concluded that model there is heteroscedasticity. In
addition, spatial correlation testing is also done
between the errors by using Moran Index. The test
results show that there is negative spatial
autocorrelation with IM = -0.0807, which means
different errors in adjacent locations and the errors
tend to spread. There is an indication of the spatial
effect of the Moran Index so that an analysis to test
for the effect is necessary. This test uses Lagrange
Multiplier (LM) lag and LM error statistics. The LM
lag statistic value is and the LM error
value is and each is compared with the
chi square table value of 3.841. The conclusion
obtained shows that there is no spatial effect of lag
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