1) The document analyzes factors that influence carbon emissions in China using regression analysis. 2) It finds that GDP, energy efficiency, energy structure, and industry structure have a significant impact on carbon emissions in China. 3) The government should emphasize advanced technology development and optimizing industrial structure to reduce carbon emissions.

1. Researchonthe Factors that Influence CarbonEmission inChina
Research on
the Factors
that Influence
Carbon
Emission
in China
Shuang Zheng

2. Research on the Factors that Influence Carbon Emission in China
1
Abstract
Greenhouse effect is a hot topic in recent years all over the world. It is related
with everyone’s daily life. I make the research on the factors that influence
carbon emission in China by doing regression analysis. By finding out that
GDP, energy efficiency and energy structure of China have a significant impact
on carbon emission, I think that the government should emphasize on the
development of advanced technology and the optimization of industrial
structure.

3. Research on the Factors that Influence Carbon Emission in China
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Contents
1. Introduction .............................................................................................................. 3
1.1 Background........................................................................................................ 3
1.2 Aim..................................................................................................................... 3
2. Research Design...................................................................................................... 4
2.1 Selection of the Dependent and Independent Variables...................................... 4
2.2 Expectation ........................................................................................................ 5
2.3 Data Collection and Processing.......................................................................... 5
2.3.1 Data Collection of Independent Variables ................................................. 5
2.3.2 Calculation of Carbon Emission................................................................ 6
3. Model Establishment and Modification...................................................................... 7
3.1 Function Form Design ........................................................................................ 7
3.1.1 Test for Linearity....................................................................................... 7
3.1.2 Logarithmic Model .................................................................................... 8
3.2 Test for Outliers .................................................................................................. 9
3.3 Regression (1st time) .......................................................................................... 9
3.4 Ramsey Reset Test .......................................................................................... 10
3.5 Regression (2nd time)........................................................................................ 10
3.6 Hypothesis Tests.............................................................................................. 12
3.6.1 Test for Heteroscedasticity...................................................................... 12
3.6.2 Test for Normality.................................................................................... 13
3.6.3 Test for Zero Mean Value........................................................................ 14
3.6.4 Test for Auto-correlation.......................................................................... 14
4. Interpretation.......................................................................................................... 16
5. Conclusion ............................................................................................................. 16
6. Limitations .............................................................................................................. 16
Appendix....................................................................................................................... 18

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1. Introduction
1.1 Background
Recently, the weather condition in Shanghai is so bad that many old people
and children have been to hospitals for asthma, respiratory infection etc. A
research conducted by Peking University indicated that every year more than
8500 people died because of fog and haze in Beijing, Shanghai and Xian. On
December.23, 2013, PM2.5of Shanghai was higher than 600 and fog red
warning was released, which had a great impact on citizens’ life and health.
In addition, greenhouse effect is also a big problem that bothers people all over
the world. The warming weather resulted in the rise of sea level and the
appearance of some vital virus. World’s agriculturemay also be affected
because of desertification andhot weather.
The two circumstances were both largely caused by carbon emission. PM2.5
was closely related to the CO2 produced by heating in winter and industrial
emission. And greenhouse gases like CO2 also contribute to greenhouse
effect. It is time to reduce the emission of carbon, especially CO2.
China consumes about 18% of world total coal consumption every year and it
is the second largest coal consumption country. It is calculated that in 2012,
China released about 2 billion tons CO2. So it is an urgent task for China to
reduce its carbon emission and protect the environment.
1.2 Aim
The aim of the report is to analyze the factors that influence the carbon
emission of China, which may contribute to formulate scientific and reasonable

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policies of energy-saving and emission-reducing.
2. Research Design
To explore the factors that influence carbon emission in China, I decide to
establish a multiple regression model.
2.1 Selection of the Dependent and Independent Variables
In order to choose appropriate variables, I read a lot of literature about study
on the factors that influence carbon emission. Scholars in China, such as Tan
Dan (2008) and DuanYing (2010), improved that the amount of carbon
emission is related to the structure of industry. Xu Dafeng(2010) found that
most of the carbon emission of China is from the second industry. Foreign
scholars have made researches on the influence of social development. For
example, Johan Albrechta, DelphineFrancois, KoenSchoors(2002) analyzed
the carbon emission of four countries from 1960 to 2006 using the Shapley
decomposition methodand found that energy efficiency, economic
development and population growth had large impact on carbon emission.
According to the researches, I finally choose carbon emission (CO2) as the
dependent variable. The following 8 variables are included as independent
variables: GDP (GDP), population (POPU), income level (INCOME), energy
structure (STRU), total export-import volume (NX), industry structure (SEC),
energy efficiency (EFFI) and car production (CAR).
GDP describes the comprehensive economic strength of a country and POPU
indicates the population growth. I use INCOME to measure people’s income
level, which equals to sum ofrural per capita net income and urban per capita
disposable income. I use the ratio of coal consumption to total energy
consumption to measure energy structure (STRU) and energy efficiency (EFFI)

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is valued by the amount of energy consumed per unit of GDP. NX describes
the situation of foreign trade. Industry structure (SEC) is replaced by ratio of
added value of the second industry to GDP. CAR reflects the amount of carbon
generated by cars, which is one of the sources of carbon emission.
2.2 Expectation
With reference to the related literature and theories, I make following
expectations:
(1) The increase of GDP, population, foreign trade and income level will lead to
more carbon emission because the development of society means more
human activities. I think that the increase of GDP should be the main cause
of the growth of carbon emission.
(2) Taking into account the fact that in China, the second industry emits most of
the carbon, I think that the industry structure should also have a
significantly positive influence on carbon emission.
(3) If the ratio of coal consumption to total energy consumption is larger, then
there will be more carbon emission. The more energy is consumed per unit
of GDP, the lower the energy efficiency is, which means more carbon
emission. So the coefficients of STRU and EFFI should both be positive.
(4) Car production should have a large influence on carbon emission because
of the car exhaust.
2.3 Data Collection and Processing
2.3.1 Data Collection of Independent Variables
I collect annual data (1978-2012) of the whole country from the statistics

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database of China economic information network. Our sample size is 35 and
there is no missing data. To eliminate the impact of price changes, I adjust
GDP, rural per capita net income and urban per capita disposable income
using the fixed base price index (1978=100). (See appendix 1)
2.3.2 Calculation of Carbon Emission
With reference to the carbon emissions decomposition model established by
Xu Guoquan, I take the following formula to calculate the annual carbon
emission of China from 1978 to 2012:
In this formula, TC represents the total amount of carbon emission and Ci
represents the carbon emission generated by the ith category of energy. is
the carbon emission index of the ithcategory of energy, is the ratio of the ith
category of energy to total energy and E is the total consumption of energy.
Considering the condition of energy consumption of China, I only take into
account the carbon emission of coal, oil and gas. By reading the literature, I
collect the carbon emission index provided by some research organizations
and use the mean value to do the calculation. (See Table 1)
Table 1 Carbon Emission Index

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Using the carbon emissions decomposition model and the data collected from
the statistics database of China economic information network, I calculate the
amount of carbon emission of China from 1978 to 2012. (See appendix 2)
3. Model Establishment and Modification
After selecting variables and collecting data, I start to establish the regression
model.
3.1 Function Form Design
3.1.1 Test for Linearity
Firstly, I draw some scatter plots of dependent variable and independent
variables and observe the relationship between them. (See Graph 1) I can see
that the independent variables are not linear with the dependent variable,
especially EFFI, SEC and STRU. So I consider establishing a logarithmic
model to meet the requirement of linearity.
Graph 1 Scatter plots

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To find out whether the linear model or the logarithmic model is better, I do the
MWD test. (See appendix 3)
Following is the process:
(1) Estimate the linear model and obtain the estimated CO2 values, that is, ye.
(2) Estimate the logarithmic model and obtain the estimated ln CO2 values,
that is, yee.
(3) Obtain z=log(ye)-yee
(4) Regress CO2 on the independent variables and z.
(5) Obtain zz=exp(yee)-ye
(6) Regress lnCO2 on the independent variables’ logarithmic form and zz.
Under the significance level of 95%, Z is statistically significant and ZZ is not,
so I should reject H0: Y is a linear function of the X’s and should not reject H1:
ln Y is a linear function of the X’s or a log of the X’s.
That is to say, I should establish a logarithmic regression model so I do a
logarithmic transformation to our data. (See appendix 4) Now the dependent
variable is lnCO2, independent variables are lnGDP, lnEFFI, lnCAR,
lnINCOME, lnNX, lnPOPU, lnSEC and lnSTRU.
3.1.2 Logarithmic Model
Following is our logarithmic model:
lnCO2=B0+B1*lnGDP+B2*lnEFFI+B3*lnCAR+B4*lnINCOME+B5*lnNX
+B6*lnPOPU+B7*lnSEC+B8*lnSTRU+μ
In this formula, Bo is the intercept and B1~B8 measures the elasticity of CO2

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with respect to the variables. μ is the stochastic error.
3.2 Test for Outliers
Before doing the regression, I make a box-plot to find out whether there are
outliers in the data or not. (See Graph 2)
Graph 2 Box-plot
3.3 Regression (1st time)
Then I start to do the regression. (See appendix 5) The fitting results are as
follows:

2ln CO = -1.007+0.005*lnNX+0.001*lnCAR-0.053*lnPOPU+0.08*lnINCOME
+0.3*lnSTRU+1.005*lnEFFI+0.076*lnSEC+0.888*lnGDP
Se=(1.785) (0.007) (0.006) (0.144) (0.034) (0.026) (0.014) (0.034) (0.038)
p-value=(0.578) (0.465) (0.839) (0.714) (0.026) (0.000) (0.000) (0.034) (0.000)
R^2=0.999966491
I find that although the R^2 is very high, there are many variables which are
not significant. What’s more, the VIF is very high, especially those of lnGDP
and lnINCOME. So I think that there exists multicollinearity between the

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explanatory variables. What’s more, from the residual scatter plot, I find that
the residual may show some pattern. (See graph 3)
Graph3 Residual Plot
3.4 Ramsey Reset Test
To find out whether the model is correctly specified, I decide to do the Ramsey
reset test. (See appendix 6) The p-value is 0.0593, which indicates that the
model has been well specified. So I think the major problem is that the model is
an over-fitting model, which leads to multicollinearity.
3.5 Regression (2nd time)
I reconsider the model and decide to eliminate the variable lnINCOME
because lnGDP can also reflect the income level to some extent. Then I do the
regression again. (See appendix 7) The results are as follows:

2ln CO = -3.99-0*lnNX+0.007*lnCAR+0.188*lnPOPU+0.301*lnSTRU
+0.997*lnEFFI+0.110*lnSEC+0.967*lnGDP
Se=(1.361) (0.007) (0.006) (0.109) 0.028) (0.015) ( 0.033) (0.018)

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p-value=(0.007) (0.900) (0.235) (0.098) (0.000) (0.000) (0.002) (0.000)
R^2=0.999959
The model is significantly better than the previous one but the VIFs are still
very large. The VIF of lnNX is 402.211. So I eliminate this variable because the
foreign trade condition can also be measured by GDP to some extent and the
coefficient of lnNX is 0.
The results of our new regression are as follows:

2ln CO = -3.846-0.007*lnCAR+0.176*lnPOPU+0.3*lnSTRU+0.996*lnEFFI
+0.108*lnSEC+0.966*lnGDP
Se=(0.731) (0.005) (0.063) (0.027) (0.013) (0.029) (0.016)
p-value=(0.000) (0.224) (0.010) (0.000) (0.000) (0.001) (0.000)
R^2=0.999959
I find that some of the VIFs are still larger than 100 and the coefficient of
lnCAR is negative, which does not meet our original expectation. I think that
the multicollinearity still exists, so I eliminate lnCAR because lnSEC, the
proportion of the second industry, can also reflect the car use condition in
China to some extent.
Then I get another regression result:

2ln CO = -3.547+0.159*lnPOPU+0.311*lnSTRU+0.981*lnEFFI+0.106*lnSEC
+0.952*lnGDP
Se=(0.697) (0.062) (0.025) (0.004) (0.030) (0.012)
p-value=(0.000) (0.016) (0.000) (0.000) (0.001) (0.000)
R^2=0.999957

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Although the variables are all statistically significant, the VIFs of lnPOPU and
lnGDP are still very large, about 100. However, according to the literature I
have found, the population growth and economic growth both have a large
influence on carbon emission. Finally I decide to eliminate lnPOPU because I
think that the influence of population growth on carbon emission is not as large
as that of GDP.
The final regression model is:

2ln CO = -1.825+0.325*lnSTRU+0.982*lnEFFI+0.051*lnSEC+0.983*lnGDP
Se=(0.180) (0.027) (0.005) (0.022) (0.002)
p-value=(0.000) (0.000) (0.000) (0.028) (0.000)
R^2=0.999947
Now all the variables are statistically significant and all the coefficients meet
our original expectation. The VIFs of the independent variables are very small,
approximate 2. I think that this model is a very good estimation of carbon
emission.
3.6 Hypothesis Tests
3.6.1 Test for Heteroscedasticity
To check whether the variance of error term is constant or not, I draw a
residual plot, the residual versus the estimation of the dependent variable.
(See Graph 4)

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Graph 4 Residual Plot
I also do White’s general heteroscedasticity test, the p-value is 0.0751. Under
the significance level of 95%, I should not reject the H0: There is no
heteroscedasticity. (See appendix 8)
Combining the residual plot and the result of White’s general
heteroscedasticity test, I confirm that the variance of error term is constant.
3.6.2 Test for Normality
From the histogram of residual, I find that the error term is approximately
normal. What’s more, the p-value of Jarque-Bera test is 0.47, so I should not
reject the H0: The sample obeys normal distribution, which means that the
normality of the error term distribution is confirmed. (See graph 5)
Graph 5 Histogram of Residual

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3.6.3 Test for Zero Mean Value
Then I draw the residual plot, residual versus time, to check that the error term
has a zero mean value. (See graph 6)
Graph 6 Residual Plot
I find that there is no outliers which are larger than +2se or -2se, so the test for
zero mean value is passed.
3.6.4 Test for Auto-correlation
From graph 5, I find that the residual may have autocorrelation. What’s more,
the Durbin-Watson value of the model is 0.729, lower than the critical dL
(1.160), which means that the error terms are correlated. To apply remedies for
the assumption violation, I use the Cochrane-Orcutt iterative procedure to
modify our regression model until the Durbin-Watson value is larger than dU
(1.803), which means that there is no autocorrelation. (See appendix 9) The
residual plot also indicates that the error term has no auto-correlation.
(See graph 7)

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Graph 7 Residual Plot
The residuals of the modified regression model are more normally distributed
than those of the previous model according to the bell-shaped histogram and
the p-value of Jarque-Bera test which is 0.99. (See graph8)
Graph 8 Histogram of Residual
The modified regression model is:

2ln CO = -2.281+0.985*lnEFFI+0.990*lnGDP+0.038*lnSEC+0.425*lnSTRU
+ [AR (1) = 0.834, AR (2) = - 0.208]
Se=(0.008) (0.004) (0.029) (0.043) (0.230) (0.206) (0.180)
p-value=(0.000) (0.000) (0.198) (0.000) (0.000) (0.000) (0.259)
R^2=0.999972

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4. Interpretation
The modified regression model meets our original expectation that GDP is
positively related with carbon emission and it is the main factor that influences
carbon emission. I find that energy efficiency and energy industry are also
major factors that influence carbon emission. The impact of industry structure
is not so large.
If energy efficiency increases by 1%, which means that the energy consumed
per unit of GDP increases by 1%, then carbon emission will increase by 98.5%.
If GDP increases by 1%, then carbon emission will increase by about 99%. If
ratio of added value of the second industry to GDP increases by 1%, then
carbon emission will increase by 3.8%. If the ratio of coal consumption to total
energy consumption increases 1%, then carbon emission will increase 42.5%.
5. Conclusion
According to our research, I think that the government should pay more
attention to increasing energy efficiency and optimizing energy structure of our
country. For example, technology of energy utilization of China can still be
improved and I should make full use of low carbon energy such as wind.
6. Limitations
However, our model still has some limitations.
Firstly, the sample size is small. Considering that the preferred ratio of
observations to variables is 15:1, I’d better do the regression with a sample
size of more than 60. Now I only have 35 observations.

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Secondly, I handle the problem of multicollinearity by eliminating independent
variables according to their meanings and VIFs. In fact there are some other
ways to solve this problem such as ridge regression analysis and principal
component analysis. It should be better if I can use them.
Thirdly, I find that lnSEC is not statistically significant after modifying the
regression model using Cochrane-Orcutt iterative procedure while this variable
should have a large influence on carbon emission according to researches
done by some scholars.

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Appendix
1. Data of Independent Variables
GDP(*10^8yuan)POPU(*10^4)INCOME STRU(%)NX(*10^8yuan)SEC(%) EFFI(ton/10^4 yuan)CAR(*10^4)
1978 3645.22 96259 477 70.7 355 47.88 15.67642008 14.91
1979 3775.63 97542 484.3 71.3 454.6 47.1 15.51740224 18.57
1980 3918.30 98705 513.7 72.2 570 48.22 15.38294611 22.23
1981 4006.52 100072 524.6 72.7 735.3 46.11 14.83756558 17.56
1982 3998.01 101654 533.3 73.7 771.3 44.77 15.52447434 19.63
1983 4039.74 103008 540 74.2 860.1 44.38 16.34760383 23.98
1984 4240.03 104357 553.3 75.3 1201 43.09 16.72252551 31.64
1985 4674.19 105851 608.6 75.8 2066.7 42.89 16.40541854 43.72
1986 4894.11 107507 645.8 75.8 2580.4 43.72 16.51986389 36.98
1987 5147.32 109300 694.8 76.2 3084.2 43.55 16.83051513 47.18
1988 5770.16 111026 822.7 76.2 3821.8 43.79 16.11687031 64.47
1989 6263.53 112704 949.5 76.1 4156 42.83 15.47594729 58.35
1990 6626.61 114333 982.9 76.2 5560.1 41.34 14.8949487 51.4
1991 7081.80 115823 1024 76.1 7225.8 41.79 14.65488479 71.42
1992 7662.43 117171 1103 75.7 9119.6 43.45 14.24743863 106.67
1993 8823.99 118517 1276 74.7 11271 46.57 13.14518089 129.85
1994 10644.17 119850 1598 75 20381.9 46.57 11.53091464 136.69
1995 12103.55 121121 1887 74.6 23499.9 47.18 10.83780865 145.27
1996 12881.47 122389 2065 73.5 24133.8 47.54 10.49507143 147.52
1997 13076.74 123626 2132 71.4 26967.2 47.54 10.39318908 158.25
1998 12960.44 124761 2118 70.9 26849.7 46.21 10.50766713 163
1999 12795.47 125786 2090 70.6 29896.2 45.76 10.98584127 183.2
2000 13055.40 126743 2103 69.2 39273.2 45.92 11.14717318 207
2001 13323.51 127627 2118 68.3 42183.6 45.15 11.28876515 234.17
2002 13403.51 128453 2100 68 51378.2 44.79 11.89472029 325.1
2003 13750.30 129227 2123 69.8 70483.5 45.97 13.36639469 444.39
2004 14702.94 129988 2199 69.5 95539.1 46.23 14.51790905 509.11
2005 15279.28 130756 2249 70.8 116921.8 47.37 15.44553301 570.49
2006 15861.04 131448 2288 71.1 140974 47.95 16.30889421 727.89
2007 17072.39 132129 2396 71.1 166863.7 47.34 16.43050398 888.89
2008 18397.83 132802 2535 70.3 179921.47 47.45 15.84143391 930.59
2009 18286.32 133450 2517 70.4 150648.06 46.24 16.76919794 1379.53
2010 19500.28 134091 2600 68 201722.15 46.67 16.66330337 1826.53
2011 21022.18 134735 2741 68.4 236401.99 46.59 16.55402682 1841.64
2012 21419.90 135404 2815 66.6 244160.21 45.32 16.88765889 1927.62

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2. Calculation of Carbon Emission
E(*10^4 ton) ratio of coal ration of oil ratio of gas TC(*10^4 ton)
1978 57144 70.7 22.7 3.2 37293.95
1979 58588 71.3 21.8 3.3 38227.15
1980 60275 72.2 20.7 3.1 39308.64
1981 59447 72.7 20 2.8 38682.40
1982 62067 73.7 18.9 2.5 40386.81
1983 66040 74.2 18.1 2.4 42894.90
1984 70904 75.3 17.4 2.4 46349.52
1985 76682 75.8 17.1 2.2 50215.59
1986 80850 75.8 17.2 2.3 53023.05
1987 86632 76.2 17 2.1 56900.07
1988 92997 76.2 17 2.1 61080.62
1989 96934 76.1 17.1 2.1 63649.09
1990 98703 76.2 16.6 2.1 64611.58
1991 103783 76.1 17.1 2 68103.13
1992 109170 75.7 17.5 1.9 71514.54
1993 115993 74.7 18.2 1.9 75585.45
1994 122737 75 17.4 1.9 79709.09
1995 131176 74.6 17.5 1.8 84825.09
1996 135192 73.5 18.7 1.8 87230.07
1997 135909 71.4 20.4 1.8 86883.36
1998 136184 70.9 20.8 1.8 86862.51
1999 140569 70.6 21.5 2 90009.56
2000 145530.86 69.2 22.2 2.2 92383.86
2001 150406 68.3 21.8 2.4 94288.02
2002 159431 68 22.3 2.4 100035.14
2003 183792 69.8 21.2 2.5 116695.42
2004 213456 69.5 21.3 2.5 135181.04
2005 235996.65 70.8 19.8 2.6 149844.18
2006 258676 71.1 19.3 2.9 164421.97
2007 280508 71.1 18.8 3.3 177995.79
2008 291448 70.3 18.3 3.7 182925.30
2009 306647 70.4 17.9 3.9 192269.82
2010 324939.15 68 19 4.4 200699.97
2011 348001.65 68.4 18.6 5 216062.39
2012 361732 66.6 18.8 5.2 220545.11

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3. Process of MWD Test
(1) Linear Model Regression Result
(2) Logarithmic Model Regression Result

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(3) Regression of CO2 on variables and z
(4) Regression of lnCO2 on variables’ logarithmic form and zz

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4. Data of variables’ logarithmic form
Year lnNX lnCAR lnPOPU lnINCOME lnSTRU lnEFFI lnSEC lnGDP lnCO2
1978 5.87 2.70 11.47 6.17 4.26 2.75 3.87 8.20 10.53
1979 6.12 2.92 11.49 6.18 4.27 2.74 3.85 8.24 10.55
1980 6.35 3.10 11.50 6.24 4.28 2.73 3.88 8.27 10.58
1981 6.60 2.87 11.51 6.26 4.29 2.70 3.83 8.30 10.56
1982 6.65 2.98 11.53 6.28 4.30 2.74 3.80 8.29 10.61
1983 6.76 3.18 11.54 6.29 4.31 2.79 3.79 8.30 10.67
1984 7.09 3.45 11.56 6.32 4.32 2.82 3.76 8.35 10.74
1985 7.63 3.78 11.57 6.41 4.33 2.80 3.76 8.45 10.82
1986 7.86 3.61 11.59 6.47 4.33 2.80 3.78 8.50 10.88
1987 8.03 3.85 11.60 6.54 4.33 2.82 3.77 8.55 10.95
1988 8.25 4.17 11.62 6.71 4.33 2.78 3.78 8.66 11.02
1989 8.33 4.07 11.63 6.86 4.33 2.74 3.76 8.74 11.06
1990 8.62 3.94 11.65 6.89 4.33 2.70 3.72 8.80 11.08
1991 8.89 4.27 11.66 6.93 4.33 2.68 3.73 8.87 11.13
1992 9.12 4.67 11.67 7.01 4.33 2.66 3.77 8.94 11.18
1993 9.33 4.87 11.68 7.15 4.31 2.58 3.84 9.09 11.23
1994 9.92 4.92 11.69 7.38 4.32 2.45 3.84 9.27 11.29
1995 10.06 4.98 11.70 7.54 4.31 2.38 3.85 9.40 11.35
1996 10.09 4.99 11.71 7.63 4.30 2.35 3.86 9.46 11.38
1997 10.20 5.06 11.73 7.67 4.27 2.34 3.86 9.48 11.37
1998 10.20 5.09 11.73 7.66 4.26 2.35 3.83 9.47 11.37
1999 10.31 5.21 11.74 7.65 4.26 2.40 3.82 9.46 11.41
2000 10.58 5.33 11.75 7.65 4.24 2.41 3.83 9.48 11.43
2001 10.65 5.46 11.76 7.66 4.22 2.42 3.81 9.50 11.45
2002 10.85 5.78 11.76 7.65 4.22 2.48 3.80 9.50 11.51
2003 11.16 6.10 11.77 7.66 4.25 2.59 3.83 9.53 11.67
2004 11.47 6.23 11.78 7.70 4.24 2.68 3.83 9.60 11.81
2005 11.67 6.35 11.78 7.72 4.26 2.74 3.86 9.63 11.92
2006 11.86 6.59 11.79 7.74 4.26 2.79 3.87 9.67 12.01
2007 12.02 6.79 11.79 7.78 4.26 2.80 3.86 9.75 12.09
2008 12.10 6.84 11.80 7.84 4.25 2.76 3.86 9.82 12.12
2009 11.92 7.23 11.80 7.83 4.25 2.82 3.83 9.81 12.17
2010 12.21 7.51 11.81 7.86 4.22 2.81 3.84 9.88 12.21
2011 12.37 7.52 11.81 7.92 4.23 2.81 3.84 9.95 12.28
2012 12.41 7.56 11.82 7.94 4.20 2.83 3.81 9.97 12.30

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5. Results of Regression (1st time)

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6. Ramsey Reset Test

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7. Regression (2nd time)
(1) Eliminate lnINCOME
(2) Eliminate lnNX
(3) Eliminate lnCAR

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(4) Eliminate lnPOPU (final model)

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8. White’s general heteroscedasticity test

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9. Cochrane-Orcutt iterative procedure
(1) 1st order
(2) 2nd order