对中国经济增长影响因素的实证分析

1、影响中国经济增长因素的实证分析学院：经济学院专业：金融教学号： 21140731姓名：王月影响中国经济增长因素的实证分析摘要：改革开放以来， 中国的社会经济取得了飞速发展， 经济增长速度更是举世 瞩目，已成为世界第二大经济体，仅次于美国。本文根据计量经济学、中级宏观 经济学、 Eviews 软件相关知识，采用时间序列数据模型和多元线性回归分析方 法对 1985年-2015 年三十多年间中国经济增长因素进行研究，分析了居民消费 价格指数、固定资产投资、公共预算支出、进出口总额对国内生产总值（GDP）的影响，建立计量经济学模型， 寻求这些变量与国内生产总值的数量关系， 进行 定量分析，对模型进行检

2、验，最终得出结论。关键词：CPI、GDP投资、预算支出、进出口、经济增长一、研究的目的要求（一）经济增长理论经济增长是指一个国家生产商品和劳务能力的扩大。 在实际核算中， 常以一国 生产的商品和劳务总量的增加来表示， 即以国民生产总值和国内生产总值 （GDP） 的增长来计算。 经济增长是经济学研究的永恒主题。 古典经济增长理论以社会财 富的增长为中心，指出生产劳动是财富增长的源泉。 现代经济增长理论认为知识、 人力资本、技术进步是经济增长的主要因素。（二）影响因素的分析在曼昆中级宏观经济学第七版中指出，国民收入核算把GDP分为四大类支出：消费（C）、投资（I）、政府购买（G）、净出口（ NX）

3、。用丫代表GDP有，丫二C+I+G+NX从公式可知，GDP主要受这四方面影响，因此本文用公共预算支出衡量一部分政 府购买，用全社会固定资产投资总额衡量投资。居民消费需求也是经济增长的主 导因素。经济增长问题既受各国政府和居民的关注也是经济学理论研究的一个重 要方面。在过去的几十年里，我国经济年均增长率高达9.6%,综合国力大大增强， 居民收入水平与生活水平不断提高，居民的消费需求的数量和质量有了很大的提 高。但是,我国目前仍然面临消费需求不足问题。 因此，研究消费需求对经济增长 的影响，并对我国消费需求对济增长的影响程度进行实证分析，可以更好的理解 消费对我国经济增长的作用。所以，选取了 CP

4、I物价指数来进行进一步分析。同 时随着对外经济加强，进出口贸易已成为中国经济重要组成部分， 所以进出口额 也是值得分析的因素。二、模型设定与参数设计（一）数据的收集中国经济增长影响因素模型时间序列表年份GDP亿元）CPI(%)全社会固定 资产投资总 额（亿元）一般公共预 算支出（亿 元）进出口总额（亿美元）19859064.6109.32543.192004.25696198610308106.53120.632204.91738.5198712094.2107.33791.692262.18826.5198815095.1111.84753.842491.211027.8198917098.

5、91184410.382823.781116.8199018824.8103.14517.453083.591154.4199121940.2103.45594.553386.621357199227082106.48080.13742.21655.3199335450.4114.712457.884642.31957199448370.3124.116370.335792.622366.2199560146.5117.120019.266823.722808.6199670538.3108.322974.037937.552898.8199778517.3102.824941.119233.

6、563251.6199883505.799.228406.1710798.183239.5199988989.898.629854.7113187.673606.3200098562.2100.432917.7315886.54742.92001108683.4100.737213.4918902.585096.5200211976599.243499.922053.156207.72003135718.9101.255566.6124649.958509.92004160289.7103.970477.4528486.8911545.52005184575.8101.888773.62339

7、30.2814219.12006217246.6101.5109998.1640422.7317604.42007268631104.8137323.9449781.3521765.72008318736.7105.9172828.462592.6625632.62009345046.499.3224598.876299.9322075.42010407137.8103.3278121.989874.16297402011479576.1105.4311485.1109247.7936418.62012532872.1102.6374675.7125952.9738671.2201358319

8、6.7102.6446294.09140212.141589.92014634043.4102512020.65151785.5643030.32015676708103.456200017576829041.4资料来源：中国统计年鉴、中国政府网（二）模型设计为了具体分析各要素对我国经济增长影响的大小，我们可以用国内生产总值（Y）作为对经济发展的衡量，代表经济发展；用 CPI（XI）消费需求；用固定 资产投资总额（X2）衡量资本投入：用预算支出（X3）去代表政府购买X4代表进 出口总额。运用这些数据进行回归分析。采用的模型如下:Y= B 0+ B 1X1+B 2X2+B 3X3+B 4X4+i

9、其中，Y代表国内生产总值，X3代表 预算支出，X2代表固定资产投资，X1代表消费价格指数，X4代表进出口总额， i代表随机扰动项。通过对该模型的回归分析，得出各个变量与我国经济增长的 变动关系。三、模型检验及修正1. 可以得到如下回归分析结果：Dependent Variable: YMethod: Least SquaresDate: 06/20/16 Time: 08:55Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C83300.8048323.231.7238

10、250.0966X1-606.6547443.4283-1.3681010.1830X2-0.3189730.225021-1.4175230.1682X34.1766020.8022165.2063310.0000X43.1914390.5848195.4571420.0000R-squared0.996436Mean dependent var189284.4Adjusted R -squared0.995888S.D. dependent var204842.6S.E. of regression13135.92Akaike info criterion21.95078Sum squar

11、ed resid4.49E+09Schwarz criterion22.18207Log likelihood-335.2371 Hannan -Quinn criter.22.02617F-statistic1817.315 Durbin -Watson stat0.322178Prob(F - statistic)0.000000Y=833300.8606.6547 B 1X10.318973 B 2X2+4.18 B 3X3+3.19 B 4X4 R2=0.996436?=0.995888 F=1817.315从数据可以看出模型拟合优度很好。分别做出丫与XXXX52. 多重共线性检验X1

12、X2X3X4X11.000000-0.288341-0.314340-0.324767X2-0.2883411.0000000.9970620.932732X3-0.3143400.9970621.0000000.945955X4-0.3247670.9327320.9459551.000000从上面结果来看，X2, X3, X4之间存在高度相关性,间的回归，结果如下:Dependent Variable: YMethod: Least SquaresDate: 06/20/16Time: 09:32Sample: 1985 2015Included observations: 31Varia

13、bleCoefficientStd. Errort-StatisticProb.C1400826.620793.22.2565090.0317X1-11490.485878.258-1.9547420.0603R-squared0.116420Mean dependent var189284.4Adjusted R -squared0.085952S.D. dependent var204842.6S.E. of regression195841.6Akaike info criterion27.27034Sum squared resid1.11E+12Schwarz criterion27

14、.36286Log likelihood-420.6903Hannan -Quinn criter.27.30050F-statistic3.821017Durbin -Watson stat0.119399Prob(F - statistic)0.060314Dependent Variable: YMethod: Least SquaresDate: 06/20/16Time:09:34Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C43248.597332.91

15、65.8978710.0000X21.2404290.03685333.659130.0000R-squared0.975042Mean dependent var189284.4Adjusted R -squared0.974181S.D. dependent var204842.6S.E. of regression32914.68Akaike info criterion23.70357Sum squared resid3.14E+10Schwarz criterion23.79608Log likelihood-365.4053Hannan -Quinn criter.23.73372

16、F-statistic1132.937Durbin -Watson stat0.209259Prob(F - statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06/20/16 Time: 20:01Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C28127.894993.0775.6333790.0000X34.0086720.07782951.506430.0000R-squared

17、0.989187Mean dependent var189284.4Adjusted R -squared0.988814S.D. dependent var204842.6S.E. of regression21665.00Akaike info criterion22.86712Sum squared resid1.36E+10Schwarz criterion22.95964Log likelihood-352.4404Hannan -Quinn criter.22.89728F-statistic2652.912Durbin -Watson stat0.339632Prob(F - s

18、tatistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06/20/16Time: 20:02Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C13363.3212872.161.0381560.3078X414.180120.69533820.393120.0000R-squared0.934814Mean dependent var189284.4Adjusted R -squared0.93

19、2566S.D. dependent var204842.6Sum squared resid8.21E+10Schwarz criterion24.75612Log likelihood-380.2859Hannan -Quinn criter.24.69376F-statistic415.8795Durbin -Watson stat0.847523Prob(F - statistic)0.000000从数据可以看出丫与X3回归具有最大的可决系数，因此选 丫=28127.89+4.009X3作为初始的回归模型，逐步回归Dependent Variable: YMethod: Least S

20、quaresDate: 06/20/16 Time: 20:26Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C142300.971534.191.9892710.0565X1-1067.525667.3011-1.5997650.1209X33.9685100.07986549.689990.0000R-squared0.990092Mean dependent var189284.4Adjusted R -squared0.989385S.D. dependent

21、 var204842.6S.E. of regression21105.05Akaike info criterion22.84418Sum squared resid1.25E+10Schwarz criterion22.98295Log likelihood-351.0848Hannan -Quinn criter.22.88941F-statistic1399.055Durbin -Watson stat0.471700Prob(F - statistic)0.000000引X1模型？2提高且变量通过了显著水平为10%的t检验但参数符号与经济意义 不符Dependent Variable

22、: YMethod: Least SquaresDate: 06/20/16 Time: 20:39Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C18313.315238.7093.4957670.0016X2-0.9021900.273468-3.2990650.0026X36.8948350.8774217.8580690.0000R-squared0.992213Mean dependent var189284.4Adjusted R -squared0.99

23、1657S.D. dependent var204842.6S.E. of regression18709.98Akaike info criterion22.60327Sum squared resid9.80E+09Schwarz criterion22.74204Log likelihood-347.3506Hannan -Quinn criter.22.64850F-statistic1783.983Durbin -Watson stat1.004618Prob(F - statistic)0.000000X2同X1被舍弃Dependent Variable: YMethod: Lea

24、st SquaresDate: 06/20/16 Time: 20:41Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C21030.713414.0226.1600970.0000X33.0649690.15526819.739900.0000X43.6301200.5649856.4251580.0000R-squared0.995630Mean dependent var189284.4Adjusted R -squared0.995318S.D. depende

25、nt var204842.6S.E. of regression14016.69Akaike info criterion22.02565Sum squared resid5.50E+09Schwarz criterion22.16442Log likelihood-338.3976Hannan -Quinn criter.22.07089F-statistic3189.620Durbin -Watson stat0.189253Prob(F - statistic)0.000000引入X4模型？2变大且通过了显著性检验水平为 10%的t检验所以最终拟合结果为 Y=21030.71+3.065

26、X3+3.630X43. 异方差性检验X3与残差散点图从散点图可以看出二者没有异方差性做残差X4散点图20,000010,00020,00030,00040,00050,00015,000 -10,000 _5,000 _0-5,000-10,000-15,000-20,000-25,000X4Heteroskedasticity Test: WhiteF-statistic2.189999Prob.F(5,25)0.0875Obs*R -squared9.442279Prob.Chi-Square(5)0.0927Scaled explained SS1.688973Prob.Chi-Squ

27、are(5)0.8903Test Equation:Dependent Variable: RESIDEMethod: Least SquaresDate: 06/20/16 Time: 21:15Sample: 1985 2015Included observations: 31VariableCoefficientStd. Errort-StatisticProb.C2.75E+087.6809430.0000X33095.2528924.4070.3468300.7316X3A20.0903030.0541421.6678940.1078X3*X4-0.9053370.571022-1.

28、5854670.1254X4-34791.9028733.42-1.2108510.2373X4A22.5611311.5096231.6965370.10222R-squared0.304590Mean dependent var1.77E+08Adjusted R -squared0.165508S.D. dependent var1.19E+08S.E. of regression1.09E+08Akaike info criterion40.02581Sum squared resid2.98E+17Schwarz criterion40.30335Log likelihood-614

29、.4000Hannan -Quinn criter.40.11628F-statistic2.189999Durbin -Watson stat0.932391Prob(F - statistic)0.087469由数据可知X3,X4在显著水平为5%水平下都不显著接受原假设所以不存在异方 差4.D.W.检验已知：DW=0.189,查表得dL=1.35, dU=1.49。由此可知，存在相关性。Breusch -Godfrey Serial Correlation LM Test:S.E. of regression6319.872 Akaike info criterion20.46070S.E

30、. of regression6319.872 Akaike info criterion20.46070F-statistic110.7312 Prob. F(1,27)0.0000S.E. of regression6319.872 Akaike info criterion20.46070S.E. of regression6319.872 Akaike info criterion20.46070Obs*R -squared24.92295 Prob. Chi -Square(1)0.0000Test Equation:Dependent Variable: RESIDMethod:

31、Least SquaresDate: 06/20/16 Time: 21:33Sample: 1985 2015Included observations: 31Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb.C1057.7181542.5990.6856730.4988X30.1990460.0725182.7447830.0106X4-0.7020570.263333-2.6660370.0128RESID( -1)0.9407670.0894

32、0210.522890.0000R-squared0.803966Mean dependent var0.000000Adjusted R -squared0.782184S.D. dependent var13541.41S.E. of regression6319.872 Akaike info criterion20.46070Sum squared resid1.08E+09Schwarz criterion20.64573Log likelihood-313.1408Hannan -Quinn criter.20.52101F-statistic36.91041Durbin -Wat

33、son stat0.833123Prob(F - statistic)0.000000LM=30*0.78=23.4大于显著性水平为5%自由度为1的2分布的临界值为3.84, 所以存在一阶序列相关性Breusch -Godfrey Serial Correlation LM Test:F-statistic67.96992Prob. F(2,26)0.0000Obs*R -squared26.02284Prob. Chi -Square(2)0.0000Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 06/20

34、/16 Time: 21:46Sample: 1985 2015Included observations: 31Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb.X30.1396350.0713231.9577890.0611X4-0.4411530.266130-1.6576600.1094C315.13741455.9710.2164450.8303RESID( -1)1.3574780.1924067.0552770.0000RESID( -

35、2)-0.4885490.203815-2.3970210.0240R-squared0.839447Mean dependent var-9.74E -12Adjusted R -squared0.814746S.D. dependent var13541.41S.E. of regression5828.377Akaike info criterion20.32555Sum squared resid8.83E+08Schwarz criterion20.55684Log likelihood-310.0461Hannan -Quinn criter.20.40095F-statistic

36、33.98496Durbin -Watson stat1.621557Prob(F - statistic)0.000000LM显著但et-2不显著不存在二阶序列相关性Dependent Variable: YMethod: Least SquaresDate: 06/20/16 Time: 21:55Sample (adjusted): 1986 2015Included observations: 30 after adjustmentsConvergence achieved after 16 iterationsVariableCoefficientStd. Errort-StatisticProb.X33.0203180.19775315.273210.0000X42.5095860.24221110.361150.0000C158613.0503632.20.3149380.7553AR(1)0.9806480.06939614.131270.0000R-squared0.