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1、我国财政收入影响因素分析 计量经济学论文(eviews分析)Questions askedSince reform and opening up, Chinas economic growth is obvious to all, 29 years, from 1981 to 2009 in Chinas fiscal revenue in the high-speed growth, Chinas fiscal revenue of less than 2 trillion yuan, from 2002 in 2006 to nearly 4 trillion yuan, to break
2、 through 2.6 trillion yuan in the first half of 2007, just five years Chinas national fiscal revenue growth. Chinas ministry of finance, according to data from 2007 1 to 6 month accumulative total national revenue reached 2.611784 trillion yuan, up 30.6% year on year, equivalent to 59.3% of the targ
3、et, increased by 8.6%, year-on-year increase revenue ZengShouE creates the highest in recent years.In the first half of 2007, Chinas fiscal revenue reached 2.6 trillion yuan, which can be said to be another surprising figure after the fiscal revenue exceeded 4 trillion yuan in 2006. In the context o
4、f high economic growth, the sustained and rapid growth of fiscal revenue, especially tax revenue, has been higher than GDP growth in the same period, which has become the main reason for the growth of fiscal revenue. At present, the main body of fiscal revenue in China is tax revenue, which accounte
5、d for 95.7% of the total fiscal revenue in 2006. At present in our country tax revenue, accounts for the largest proportion of value added tax, because still rely on investment to boost the economy in our country at present stage, it also brought the results of the current our country finance income
6、 growth is faster. Presentative only actually, fiscal revenue growth too fast, and the fast investment growth of overheating caused by system problem is the need to worry about, therefore, facing the rapid growth of fiscal revenue, the worry is overheating problem also will be more and more serious.
7、 If the fiscal revenue increases significantly, much faster than the national income growth rate, there will be a series of problems.Income is the basic safeguard that a country government realizes governmental function, have special effect to national economy operation and social development. First
8、, it is a material guarantee of a countrys income. The size of a countrys fiscal revenue is often an important indicator of its economic strength. Second, fiscal revenue is an important economic lever for the state to implement macro-control over the economy. The growth of financial income is relate
9、d to the economic development and social progress of a country. Therefore, it is particularly important to study the growth of fiscal revenue. The main source of fiscal revenue is various tax revenue, in addition to other government revenue and fund revenue. At the same time, the size of a countrys
10、fiscal revenue is also affected by many factors such as the size of the economy. This paper sets up a financial income influencing factor model, analyzes the main factors influencing Chinas fiscal income empirically, and provides some policies for how to formulate Chinas fiscal income plan reasonabl
11、y and effectivelyAdvice.2. Model settingThe research on the influence factors of financial income is inseparable from some basic economic variables. The choice of regression variables is a very important problem in establishing regression model. If some important variables are omitted, the regressio
12、n equation will not work well. Considering too many variables, not only does the calculation amount increase a lot, but also the stability of the obtained regression equation is very poor, which directly affects the application of the regression equation. Through the explanation of financial income
13、and observation of practice in economic theory, the main factors influencing financial income are taxation, gross domestic product, fixed asset investment in the whole society, etc.(1) taxes. Due to the compulsory, non-remunerative and fixed characteristics of tax collection, tax can provide a suffi
14、cient source of funds for the government to perform its functions. Therefore, all countries regard it as the most important form of revenue and the most important source of revenue.(2) GDP. It is often recognized as the best indicator of a countrys economic condition. GDP will promote the national i
15、ncome, which will directly influence the amount of household savings and keep the same trend with the growth of fiscal income.(3) investment in fixed assets in the whole society. It is the economic activity that builds and buys fixed assets, namely fixed assets reproduction activity. Mainly through
16、investment to promote economic growth, expand tax sources, and then promote overall fiscal revenue growth.(4) model form designBased on fiscal revenue (one hundred million yuan) as the dependent variable Y, tax X1 (one hundred million yuan), the gross domestic product (GDP) X2 (one hundred million y
17、uan), the whole social fixed assets investment X3 (one hundred million yuan), three indicators as independent variable, build multivariate function, namely:Well, lnY is equal to C+ C1lnX1+ C2lnX2+ C3lnX3+Iii. Data collectionBased on the statistical yearbook of China, this paper USES the data of tax
18、revenue, GDP and fixed asset investment of the whole society from 1981 to 2009, which is true and reliable. In order to eliminate the heteroscedastic, do take logarithm with data, carries on the regression analysis using E - views, exclude the multicollinearity of previous models, fiscal revenue fac
19、tors more accurate model is set up, analysis the main factors influencing the fiscal revenue and its influence.Income. Chinas fiscal revenue mainly comes from the industrial, agricultural, commercial, transportation and service sectors.Therefore, this paper argues that fiscal revenue is mainly affec
20、ted by total tax revenue, gross domestic product, other income and the total number of employed people.2. Preset modelMake fiscal revenue (one hundred million yuan) for Y be explained variables, the total tax revenue X1 (one hundred million yuan), gross domestic product (GDP) X2 (one hundred million
21、 yuan), other income X3 (one hundred million yuan), employment population for X4 (ten thousand) as the explained variable, on the basis of regression model is established.Ii. Data collection二、数据收集obs 财政收入Y 总税收收入X1 国内生产总值X2 其他收入X3 就业人口总数X4 1990 2937.1 2821.86 18667.8 299.53 64749 1991 3149.48 2990.17
22、 21781.5 240.1 65491 1992 3483.37 3296.91 26923.5 265.15 66152 1993 4348.95 4255.3 35333.9 191.04 66808 1994 5218.1 5126.88 48197.9 280.18 67455 1995 6242.2 6038.04 60793.7 396.19 68065 1996 7407.99 6909.82 71176.6 724.66 68950 1997 8651.14 8234.04 78973 682.3 69820 1998 9875.95 9262.8 84402.3 833.3
23、 70637 1999 11444.08 10682.58 89677.1 925.43 71394 2000 13395.23 12581.51 99214.6 944.98 72085 2001 16386.04 15301.38 109655.2 1218.1 73025 2002 18903.64 17636.45 120332.7 1328.74 73740 2003 21715.25 20017.31 135822.8 1691.93 74432 2004 26396.47 24165.68 159878.3 2148.32 75200 2005 31649.29 28778.54
24、 184937.4 2707.83 75825 2006 38760.2 34804.35 216314.4 3683.85 76400 2007 51321.78 45621.97 265810.3 4457.96 76990 2008 61330.35 54223.79 314045.4 5552.46 774802009 68518.3 59521.59 340506.9 7215.72 77995三、 模型建立 1、 散点图分析50000100000150000200000250000300000350000010000300005000070000YX1X2X3X42、 单因素或多变
25、量间关系分析Y X1 X2 X3 X4 Y 1 0.9989134611478530.993479045290804 0.877014488679564 0.983602719841508 X1 0.998913461147853 1 0.9937402677184690.855637734744782 0.984935296593492 X2 0.993479045290804 0.993740267718469 1 0.8561835802284710.986241165680459 X3 0.877014488679564 0.855637734744782 0.856183580228
26、471 1 0.810940334650381X40.9836027198415080.9849352965934920.9862411656804590.8109403346503811由散点图分析和变量间关系分析可以看出被解释变量财政收入Y 与解释变量总税收收入X1、国内生产总值X2、其他收入X3、就业人口总数X4呈线性关系,因此该回归模型设为:+=443322110X X X X Y3、 模型预模拟由eviews 做ols 回归得到结果:Dependent Variable: Y Method: Least Squares Date: 11/14/11 Time: 17:51 Sampl
27、e: 1990 2009 Included observations: 20Variable Coefficient Std. Error t-Statistic Prob. C 7299.523 1691.814 4.314614 0.0006 X1 1.062802 0.021108 50.34972 0.0000 X2 0.001770 0.004528 0.391007 0.7013 X3 0.873369 0.119806 7.289852 0.0000 X4-0.1159750.026580-4.3631600.0006R-squared 0.999978 Mean depende
28、nt var 20556.75 Adjusted R-squared 0.999972 S.D. dependent var 19987.03 S.E. of regression 106.6264 Akaike info criterion 12.38886 Sum squared resid 170537.9 Schwarz criterion 12.63779 Log likelihood -118.8886 F-statistic 166897.9 Durbin-Watson stat1.496517 Prob(F-statistic)0.0000004321115975.087336
29、9.0001770.0062802.1523.7299X X X X Y -+=(4.314614) ( 50.34972 ) ( 0.391007) ( 7.289852) ( -4.363160) 999978.02=R 999972.02=R 9.166897=F 496517.1.=W D 四、 模型检验 1.计量经济学意义检验 多重共线性检验与解决求相关系数矩阵,得到:Correlation MatrixY X1 X2 X3 X4 1 0.998913461147853 0.9934790452908040.8770144886795640.9836027198415080.9989
30、13461110.99374026770.85563773470.984935296547853 18469 44782 934920.993479045290804 0.993740267718469 10.8561835802284710.9862411656804590.877014488679564 0.8556377347447820.856183580228471 10.8109403346503810.983602719841508 0.9849352965934920.9862411656804590.810940334650381 1发现模型存在多重共线性。接下来运用逐步回归
31、法对模型进行修正:将各个解释变量分别加入模型,进行一元回归:作Y与X1的回归,结果如下:Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:02Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -755.6610 145.2330 -5.203094 0.0001X1 1.144994 0.005760 198.7931 0.0000R-squared 0.999545 Mean
32、dependent var 20556.75Adjusted R-squared 0.999519 S.D. dependent var 19987.03S.E. of regression 438.1521 Akaike info criterion 15.09765Sum squared resid 3455590. Schwarz criterion 15.19722Log likelihood -148.9765 F-statistic 39518.70Durbin-Watson stat 0.475046 Prob(F-statistic) 0.000000作Y与X2的回归,结果如下
33、:Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:06Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -5222.077 861.2067 -6.063674 0.0000X2 0.207689 0.005548 37.43267 0.0000R-squared 0.987317 Mean dependent var 20556.75 Adjusted R-squared 0
34、.986612 S.D. dependent var 19987.03 S.E. of regression 2312.610 Akaike info criterion 18.42478 Sum squared resid 96267005 Schwarz criterion 18.52435 Log likelihood -182.2478 F-statistic 1401.205 Durbin-Watson stat 0.188013 Prob(F-statistic) 0.000000作Y与X3的回归,结果如下:Dependent Variable: YMethod: Least Sq
35、uaresDate: 11/22/11 Time: 23:08Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C 2607.879 773.9988 3.369358 0.0034X3 10.03073 0.294311 34.08209 0.0000R-squared 0.984740 Mean dependent var 20556.75 Adjusted R-squared 0.983893 S.D. dependent var 19987.03 S.E.
36、 of regression 2536.645 Akaike info criterion 18.60971 Sum squared resid 1.16E+08 Schwarz criterion 18.70929 Log likelihood -184.0971 F-statistic 1161.589 Durbin-Watson stat 1.194389 Prob(F-statistic) 0.000000作Y与X4的回归,结果如下:Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:08Sample: 1
37、990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -272959.3 37203.65 -7.336894 0.0000X4 4.097403 0.518467 7.902918 0.0000R-squared 0.776276 Mean dependent var 20556.75 Adjusted R-squared 0.763846 S.D. dependent var 19987.03 S.E. of regression 9712.824 Akaike info cr
38、iterion 21.29492 Sum squared resid 1.70E+09 Schwarz criterion 21.39449 Log likelihood -210.9492 F-statistic 62.45611 Durbin-Watson stat 0.157356 Prob(F-statistic) 0.000000依据可决系数最大的原则选取X1作为进入回归模型的第一个解释变量,再依次将其余变量分别代入回归得:作Y与X1、X2的回归,结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23
39、:09Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -188.4285 239.0743 -0.788159 0.4415X1 1.281594 0.049472 25.90568 0.0000X2 -0.025055 0.009029 -2.774908 0.0130R-squared 0.999687 Mean dependent var 20556.75Adjusted R-squared 0.999650 S.D. dependent var 19
40、987.03S.E. of regression 374.0345 Akaike info criterion 14.82405Sum squared resid 2378330. Schwarz criterion 14.97341Log likelihood -145.2405 F-statistic 27118.20Durbin-Watson stat 0.683510 Prob(F-statistic) 0.000000 作Y与X1、X3的回归,结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:1
41、0Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -351.1054 83.15053 -4.222527 0.0006X1 0.992813 0.018707 53.07196 0.0000X3 1.356936 0.165109 8.218410 0.0000R-squared 0.999908 Mean dependent var 20556.75Adjusted R-squared 0.999898 S.D. dependent var 19987.
42、03S.E. of regression 202.1735 Akaike info criterion 13.59361Sum squared resid 694859.9 Schwarz criterion 13.74297Log likelihood -132.9361 F-statistic 92839.33Durbin-Watson stat 1.177765 Prob(F-statistic) 0.000000作Y与X1、X4的回归,结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:10Samp
43、le: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C 11853.46 1824.522 6.496748 0.0000X1 1.185886 0.006645 178.4608 0.0000X4 -0.186645 0.026984 -6.917003 0.0000R-squared 0.999881 Mean dependent var 20556.75Adjusted R-squared 0.999867 S.D. dependent var 19987.03S.E
44、. of regression 230.8464 Akaike info criterion 13.85886Sum squared resid 905931.0 Schwarz criterion 14.00822Log likelihood -135.5886 F-statistic 71206.90Durbin-Watson stat 1.459938 Prob(F-statistic) 0.000000在满足经济意义和可决系数的条件下选取X3作为进入模型的第二个解释变量,再次进行回归则:作Y与X1、X3、X2的回归,结果如下Dependent Variable: YMethod: Le
45、ast SquaresDate: 11/22/11 Time: 23:13Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C -76.04458 100.1724 -0.759137 0.4588X1 1.085924 0.029801 36.43881 0.0000X3 1.210853 0.133444 9.073877 0.0000X2 -0.014073 0.003944 -3.567901 0.0026R-squared 0.999949 Mean d
46、ependent var 20556.75Adjusted R-squared 0.999939 S.D. dependent var 19987.03S.E. of regression 155.5183 Akaike info criterion 13.10826Sum squared resid 386975.0 Schwarz criterion 13.30741Log likelihood -127.0826 F-statistic 104602.9Durbin-Watson stat 1.196933 Prob(F-statistic) 0.000000作Y与X1、X3、X4的回归
47、,结果如下Dependent Variable: YMethod: Least SquaresDate: 11/22/11 Time: 23:13Sample: 1990 2009Included observations: 20Variable Coefficient Std. Error t-Statistic Prob.C 6781.764 1024.745 6.618003 0.0000X1 1.068642 0.014514 73.62764 0.0000X3 0.891069 0.107949 8.254551 0.0000X4 -0.107639 0.015451 -6.9666
48、75 0.0000R-squared 0.999977 Mean dependent var 20556.75Adjusted R-squared 0.999973 S.D. dependent var 19987.03S.E. of regression 103.7654 Akaike info criterion 12.29900Sum squared resid 172276.1 Schwarz criterion 12.49814Log likelihood -118.9900 F-statistic 234970.9Durbin-Watson stat 1.451447 Prob(F-statistic) 0.000000可见加入其余任何一个变量都会导致系数符号与经济意义不符,故最终修正后的回归模型为:Dependent Variable: YMethod: Least Square