Journal of Computers - Academy Publisher

JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 1829
As can be seen from the above analysis, the main
factors that influence China’s provincial manufacturing
agglomeration include Market demand, Transportation,
Industrial foundation, Supporting services and Financial
income. Industrial foundation and Supporting services
have positive impact on manufacturing agglomeration.
Their elasticities are 1.3435 and 1.3270. While Financial
income has a negative impact on manufacturing
agglomeration with its elasticity -0.3536. Due to the fault
of OLS method dealing with spatial autocorrelation, we
intend to use spatial econometric models to analyze the
influencing factors of manufacturing agglomeration. The
new model is the one in estimation 2 of table 3:
ln LQ = β0
+ β1
ln demand + β2
ln road + β3
ln
+ β4
ln service + β5
ln fisc − income + ε
We need to verify the existence of spatial
autocorrelation before peforming spatial econometric
analysis. The result of spatial dependence test is shown in
table 4.
Table 4: Spatial dependence test
Spatial dependence MI/DF Statistics p value
Moran’s I (Error) 0.1047 1.9679 0.0489
LMLAG 1 0.2102 0.6465
R-LMLAG 1 0.4540 0.5003
LMERR 1 0.0931 0.7602
R-LMERR 1 0.3370 0.5615
LM-SARMA 2 0.5472 0.7606
As is shown in table 4, LMERR, LMLAG, R-LMERR
and R-LMLAG do not pass the test at significance level
of 5%. And SEM is better comparing the value of LogL,
industry AIC, SC and LR of SLM and SEM. The result of
estimation of SLM and SEM ( 8)
is displayed in table 5.
Table 5: SLM and SEM estimation of the factors of manufacturing agglomeration (2006-2008)
SLM SEM
Variables
β
Standard
deviation
t value p value β
Standard
deviation
t value p value
C 1.6128 0.5600 2.8797 0.0039 1.5984 0.5300 3.0154 0.0025
lndemand 0.7757 0.1192 6.5054 0.0000 0.8018 0.0975 8.2223 0.0000
lnroad 0.0742 0.0573 1.2952 0.1952 0.0851 0.0437 1.9457 0.0516
lnindustry 1.3642 0.2306 5.9145 0.0000 1.3741 0.2224 6.1785 0.0000
lnservice 1.3462 0.5464 2.4635 0.0137 1.3585 0.5442 2.4960 0.0125
lnfisc-income 0.3370 0.2200 1.5319 0.1255 0.3516 0.2199 1.5985 0.1099
ρ/λ 0.0621 0.1208 0.5146 0.6067 0.3324 0.1275 2.6064 0.0074
Statistical test DF Statistics p value DF Statistics p value
R 2 0.9296 0.9495
LogL 4.8505 4.8034
AIC 4.2989 2.3930
SC 14.1073 10.8002
LR 1 0.2285 0.6325 1 0.1344 0.7138
As is shown in table 5, the coefficients of all variables
are positive. It indicate that this estimation is more
consistent with theoretical analysis. Industrial foundation
and Supporting services have positive impact on
manufacturing agglomeration. Their elasticities are
1.3642 and 1.3462 in SLM, 1.3741 and 1.3585 in SEM.
B. Prediction of Location Quotient of China’s Provincial
Manufacturing
BP neural network is error back propagation neural
network and feedforward network. It is widely used in
function approximation, pattern recognition and data
compression. In this article, we will predict the 2009’s
Location Quotient of China’s Provincial Manufacturing
using Neural Network Toolbox in Matlab (2006, 2007
and 2008’s location quotient are references for
comparison). The Matlab program of prediction is as
follows (take Beijing for example):
clc
close all
clear all
p0=[2.4289 2.1880 2.0585 1.6110 1.3667 1.4216
1.4169 1.3470 1.2994 1.3316];
day=1999:2008;
plot(day, p0,'b+')
© 2011 ACADEMY PUBLISHER
hold on
plot(day, p0, 'r-.')
p1=(p0-min(p0))./(max(p0)-min(p0));
for i=1:5;
p(:,i)=p1(i:i+2);
t(:,i)=p1(i+3);
end
p;
t;
for i=1:5;
testp(:,i)=p1(i+1:i+3);
testt(:,i)=p1(i+4);
end
net=newff(minmax(p),[20,1],{'logsig','purelin'},'trainl
m');
net.trainParam.lr=0.8;
net.trainParam.epochs = 500;
net.trainParam.goal = 0.001;
net=train(net,p,t);
y=sim(net,testp);
E=testt-y;