影响GDP增长的经济因素分析

1、精品文档影响GDP曾长的经济因素分析国际经济与贸易钟颀 405020491978 年十一届三中全会以后，在邓小平总设计师的指引下，中国开始了改革开放。改革开放的三十年中， 我国GDF年增长，经济发展速度令世界瞩目。为更好的了解我国经济增长的原因，现对影响我国GDP曾长的经济因素进行了分析。下表提供了我国1978 2005年的GDP及其主要影响因素的数据。其中Y=GDP亿元）；X1 =能源消费总量（万吨标准煤）；*2=就业人员（万人）；X3即民消费水平（元）；*4=农业总产值（亿元）；X5=社会消费品零售总额（亿元）；*6=进出口贸易总额（亿元）ObsX1X2X3X4X5X6Y197857144

2、4015218413971558.63553645.2175197958588410242081697.61800454.64062.5792198060275423612381922.621405704545.624198159447437252642180.622350735.34889.4611198262067452952882483.262570771.35330.4511983660404643631627502849.4860.15985.5516198470904481973613214.133376.412017243.7517198576682498734463619.49

3、43052066.79040.7366198680850512824974013.0149502850.410274.379198786632527835654675.758203084.212050.615198892997543347145865.277440382215036.823198996934553297886534.738101.4415617000.919199098703647498337662.098300.15560.118718.3221991103783654919328157.039415.67225.821826.199199210917066152111690

4、84.710993.79119.626937.276199311599366808139310995.514270.41127135260.025199412273767455183315750.518622.920381.948108.456199513117668065235520340.923613.823499.959810.529199613894868950278922353.728360.224133.870142.492199713779869820300223788.431252.926967.277653.135199813221470637315924541.933378

5、.126849.783024.281999133830.9771394334624519.135647.929896.288188.9552000138552.5872085363232917.9339105.739273.298000.4542001143199.2173025386937213.4943055.442183.6108068.222002151797.2573740410643499.9148135.951378.2119095.692003174990.374432441129691.852516.370483.51351742004203226.7752004925362

6、38.995950195539.1159586.7200522331975825543939450.8967176.6116921.8183956.1现估计模型为Y=c+A1*X1+A2*X2+A3*X3+A4*X4+A5*X5+A6*X6+U、平衡性检验和协整检验将被解释变量 丫与解释变量XI、X2、X& X4、XS X6进行多元回归，可以得出残差序 列e,通过残差序列的线性图形（表 1.1 ）:由图可知，残差序列是有截距无明显趋势的时间序列。因此选择模型2进行单位根检验，结果如下（表1.2 ）:Null Hypothesis: E has a unit rootExogenous:

7、 ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=6)t-StatisticProb.*Augmented Dickey-Fuller test statistic-3.1492810.0347Test critical values:1% level-3.6998715% level-2.97626310% level-2.627420*MacKinnon (1996) one-sided p-values.Augmented Dickey-Fuller Test EquationDependent Variable: D(E)Me

8、thod: Least SquaresDate: 12/17/07 Time: 22:15Sample (adjusted): 1979 2005Included observations: 27 after adjustmentsCoefficieVariablentStd. Errort-StatisticProb.E(-1)-0.5340390.169575-3.1492810.0042C-29.52296117.8645-0.2504820.8043R-squared0.284036Mean dependent var-18.20297Adjusted R-squared0.25539

9、8S.D. dependent var709.4155S.E. of regression612.1570Akaike info criterion15.74304Sum squared resid9368406.Schwarz criterion15.83903Log likelihood-210.5311F-statistic9.917968Durbin-Watson stat1.880914Prob(F-statistic)0.004207由上面的结果可以看出，残差序列e在显著性水平为0.05的条件下，没有通过显著性检验,即e平稳，因此可以得出原模型协整，可以进行下面的回归。二、多重共线

10、性检验1、检验：利用OLS对以上参数进行估计，结果如下(表 2.1.1 )Dependent Variable: YMethod: Least SquaresDate: 12/11/07 Time: 20:56Sample: 1978 2005Included observations: 28CoefficieVariablentStd. Errort-StatisticProb.C526.04171206.6350.4359570.6673X1-0.0681080.027789-2.4508890.0231X20.0601750.0547801.0985010.2844X312.913811

11、.8696506.9070750.0000X4-0.0405530.054016-0.7507710.4611X50.9890140.1968575.0240230.0001X60.4992210.04415411.306380.0000R-squared0.999887Mean dependent var51166.32Adjusted R-squared0.999855S.D. dependent var52735.89S.E. of regression635.9707Akaike info criterion15.96050Sum squared resid8493633.Schwar

12、z criterion16.29355Log likelihood-216.4470F-statistic30938.68Durbin-Watson stat1.728146Prob(F-statistic)0.000000结果分析：可决系数为0.999855 , F统计量为30938.68 ,通过F检验，表明模型拟合优度较好。 对于 A1、A2、A3、A4 A5、A6, X2和X4白T统计量均小于临界值 T0.025(21)=0.435957, 而X1和X4的系数为负，与经济意义和实际情况不符。因此，可初步认为此模型存在严重的多重共线性。六个解释变量的如下简单相关系数矩阵(表2.1.2)X1

13、X2X3X4X50.908488758720.959220601010.912159453790.96155636128X1165756032197568050.908488758720.875303150110.870880188510.84947942717X2657515327046354850.959220601010.875303150110.976677694210.99608847436X3603253271081447970.912159453790.870880188510.976677694210.96762964145X4197504630814104310.961556

14、361280.849479427170.996088474360.96762964145X5680554854797043110.937316682820.729465723390.926876994820.867243928970.95329264272X691664794322515645825X60.9373166828291660.7294657233947940.9268769948232250.8672439289715640.953292642725825110欢在下载从上表可以看出，各解释变量之间存在高度线性相关。同时由表1.2又可看出，尽管整体上线性回归拟合较好，但X2, X

15、4变量的参数T值并不显著，表明模型中解释变量确实存在严 重的多重共线性。2、修正：运用OLS方法逐一求出 Y对各个解释变量的回归，结果如下(表 2.2.1 )变量参数估计 值t统计量RA(2)一RA(2)x11.15808518.445350.9290070.926276x23.6126497.7580080.6983290.686727x331.2236939.251380.9834040.982766x43.69423416.813360.9157730.912533x52.64849596.499460.9972160.997109x61.70401319.288870.9346830.

16、932171综合分析可见，在六个R回归模型中，加入X5的方程一RA(2)最大，以X5为基础，顺次加入其他变量逐步回归，结果如表(表2.2.2 ):x1x2x3x4x5x6x5 x10.0578862.525631(+1.294999)(+25.59707)x5 x2-0.1962132.750751(-2.546525)(+58.19202)x5 x3-12.232623.674864RA(2)0.9971820.9976120.998266(-4.283421)(+15.27659)x5 x4x5 x6-0.564964(-4.900573)3.024076(+38.18075)2.2376

17、38(+65.79107)0.286418(-12.67189)0.9984660.999595经比较，新加入 X6的方程RA(2)= 0.999595,改进最大，而且各参数t值显著，选择保留X6,再加入其它新变量逐步回归，结果如下(表2.2.3 ):x1x2x3x4x5x6x5 x6 x10.0046512.2299840.284872(+0.260774)(+49.09533)(+11.97611)x5 x6 x20.0087422.228580.289556(+0.233341)(+42.81782)10.85269x5 x6 x39.4952861.2513410.418594(+5.

18、152867)(+6.487128)(+13.87153)x5 x6 x4-0.0449462.2853250.274004(-0.51554)(+23.14678)(+8.238447)RA 0.9995790.9995790.99980.999583在X5、X6基础上加入 X3后的方程RA(2)明显增大, 后，虽然一RA(2)有所上升，但参数检验不显著；加入号不合理。所以选择保留X3,继续逐步回归，结果如下x1x2x3x5 x6 x3 x1-0.04033815.43851(-3.302717)(+6.944039)x5 x6 x3 x2-0.06005411.34664(-2.26941

19、2)6.013285x5 x6 x3 x49.458897(+4.997874)而且各参数X4后，不仅(表 2.2.4)x4-0.010846(-0.174824)t值显著。加入 X1、X2t检验不显著，X4的符:x5x60.9849220.476601(+5.434718)(+15.43851)1.1212610.422805(+5.992658)(+15.1407)1.2666280.415092(+5.878828)(+11.29637)RA(2)0.9998580.9998290.999791加入X1、X2、X4后，不仅参数t值不再全部显著，参数符号也不合埋。因此，X1、X2、X45旭

20、产里多里共致住，艮才以别然。卜啊父X3、X5、X6,修正程型为:Y=C+A3*X3+A5*X5+A6*X6+U 最后修止产里线性影响的回归结果为（表2.2.5）Dependent Variable: YMethod: Least SquaresDate: 12/17/07 Time: 19:38Sample: 1978 2005Included observations: 28CoefficieStd. Errort-StatisticProb.VariablentC-1264.654249.9107-5.0604230.0000X39.4952861.8427195.1528670.0000

21、X51.2513410.1928966.4871280.0000X60.4185940.03017613.871530.0000R-squared0.999822Mean dependent var51166.32Adjusted R-squared0.999800S.D. dependent var52735.89S.E. of regression746.4204Akaike info criterion16.20002Sum squared resid13371443Schwarz criterion16.39033Log likelihood-222.8003F-statistic44

22、917.00Durbin-Watson stat0.979249Prob(F-statistic)0.000000根据回归结果，得回归模型：丫=-1264.654+9.495286X3+1.251341X5+0.418594X6t = (-5.060423 ) (5.152867 ) ( 6.487128 ) ( 13.87153 )RA(2)=0.999822 RA(2)=0.999800F=44917.00 DW=0.979249三、异方差性检验1、White 检验：表(3.1)White Heteroskedasticity Test:F-statistic0.980384Prob. F

23、(9,18)0.487547Obs*R-squared9.210473Prob. Chi-Square(9)0.418077Test Equation:Dependent Variable: RESIDA2Method: Least SquaresDate: 12/17/07 Time: 19:57Sample: 1978 2005Included observations: 28CoefficieStd. Errort-StatisticProb.VariablentC1037033.794984.11.3044700.2085X3-21662.4113609.43-1.5917210.12

24、89X3A211.3772716.261900.6996270.4931X3*X5-1.6362882.998311-0.5457370.5919X3*X60.0319320.4969710.0642530.9495X52129.3051179.7041.8049490.0878X5A20.0528100.1442880.3660050.7186X5*X6-0.0050260.048723-0.1031590.9190X6-80.84421234.4271-0.3448590.7342X6A20.0018730.0044350.4223580.6778R-squared0.328945Mean

25、 dependent var477551.5Adjusted R-squared-0.006582S.D. dependent var498819.6S.E. of regression500458.4Akaike info criterion29.35689Sum squared resid4.51E+12Schwarz criterion29.83268Log likelihood-400.9965F-statistic0.980384Durbin-Watson stat2.169849Prob(F-statistic)0.487547由表知：nRA(2)= 9.210473 ,由 Whi

26、te检验知，在 =0.05下,查x A(2)分布表，得临界值 X人(2)0.05(9)=16.919.比较计算的x人化)统计量与临界值，因为nRA(2)= 9.210473V xA(2)0.05(9)=16.919,所以接受原假设，表明模型不存在异方差。2、Arch 检验:表(3.2):ARCH Test:F-statistic0.425586Prob. F(1,25)0.520117Obs*R-squared0.451939Prob. Chi-Square(1)0.501415Test Equation:Dependent Variable: RESIDA2Method: Least Squ

27、aresDate: 12/17/07 Time: 20:14Sample (adjusted): 1979 2005Included observations: 27 after adjustmentsCoefficieVariablentStd. Errort-StatisticProb.C392816.1133202.62.9490110.0068RESIDA2(-1)0.1252500.1919920.6523700.5201R-squared0.016738Mean dependent var453325.1Adjusted R-squared-0.022592S.D. depende

28、nt var491248.6S.E. of regression496766.7Akaike info criterion29.14082Sum squared resid6.17E+12Schwarz criterion29.23680Log likelihood-391.4010F-statistic0.425586Durbin-Watson stat2.049189Prob(F-statistic)0.520117从表知，(n-p)RA(2)=0.451939 ,由Arch检验知，在 =0.05下,查x人(2)分布表，得临界值x a(2)0.05(1)=3.841,比较计算的x人(2)统

29、计量与临界值，因为(n-p)RA(2)=0.451939<xa(2)0.05(1)=3.841,所以接受原假设，表明模型不存在异方差。通过White检验和Arch检验，表明模型是不存在异方差的。四、自相关检验根据表2.2.5 , DW1为0.979249 ,查D恤计表可知，在 =0.05的水平下,dL=1.328 ,模型中DW< dL显然存在正自相关。这一点从残差图中也可以看出，如图 4.1:Residual Actual Fitted在图4.1中，残差的变动有系统模式，连续为正和连续为负，表明残差项存在一阶自相关, 模型中t统计量和F统计量的结论不可信，需采取补救措施。为解决自相关问题，选用科克伦-奥克特迭代法。利用Eviews软件，可得回归方程：e(t尸 0.467257e(t-1).由上式可知=0.467257,对原模型进行广义差分，结果如下(表 4.2 ):Dependent Variabl