国内生产总值的影响因素

1、国内生产总值的影响因素以福建省为例以下数据均来自福建省统计年鉴:年份GDP固定资产投资额财政收入出口总额工业总产值19901175.791154072570600244906531490019911428.361456253697000314746658860019921910.422275484753500438666915510019932993.36368449511058005158741522370019944229.26538866914966006430202128610019955483.28681171418458007908062638520019966419.247900

2、00021511008382392840510019977436.808984678251300010255603066760019988220.001048517828142009963873218510019998877.2510400049312570010351933479840020009870.58108247163696700129082839948600200110506.33113447564283300139223243980800200211324.01123076214762000173708652602000200312866.74150787255510000211

3、317366166100200414912.98189909746225700293947685445000200516995.93234473307881100348419599958900200619833.7431150775101277004126174118556800200724160.1643217404128284004994039144250600200828960.0253016939151651005699184171414400200932436.8163620327169463005331902186814800201038915.258273418620560100

4、7149313238053200201147739.92101194678259701009283779303305900201255107.0012709660430088009783259323799400一：提出问题宏观经济学的核心问题之一是经济增长，在经济日益发展的今 天，国内生产总值已经成为一个最重要的衡量经济发展的指标之一。随着改革开放以来，福建省与中国的经济实现了同步增长， 取得了巨大的成就，理解福建省经济发展的原因显得至关重要。 同时对GDP在福建省的深度解读将有利于福建省更好更快的发 展，以期对实现福建省跨越式发展提供对策。理论分析：哪些因素对福建省的国民生产总值有较大的影响

5、三建立模型：运用统计学以及计量经济学的方法，利用1990至2012年的统计数据，对福建省 GDP的增长因素进行实证分析，并以固定资产 投资总额TZ、财政收入CZ出口总额CK工业总产值 GY为解 释变量建立影响 GDP的多元回归模型，以阐明影响福建省GDP的主要因素。从而对福建省 GDP增长因素进行了实证分析。四：数据处理过程：（一 .）多元线性回归分析利用 EViews估计模型的参数obsobsYczCKGYTZobsobsYczCKGYTZ1990199011757901154072570600.0244906.05314900.199119911428.3601456253,P 69700

6、0.031474S06580600.199219921910.4202275404.753500.0438666,09155100.1993199329933603584495.1105800.515874.0115223700199419944229.26053886691496600.643020.021286100199519955433.2806811714.1845800790806.0263S52D0199619966419.24079000002151100,838239.028405100199719977436.8008964678.25130001025560.306676

7、00199819988220.000104B51782814200.996387.032185100199919998S77.250104000493125700.103519334798400200020009B70.&B010B247163696700.1290828.399486002001200110506.331134475642333001392232.439803002002200211324 01123076214762000.1737086.526020002003200312866,74150767255510000.2113173.66166100200420041491

8、2.93189909746225700.2939476.S54450002005200516995.93234473307881100.3484195.9995890020062006198337431150775101277004126174.1 19E+D32007200724160.1643217404128234004994039.1 44E+082008200828960.0253016939151651005699184.1 71E*082009200932436,6163620327169463005331902.1.87E+082010201038915.25827341862

9、05601007149313.2.38E*082011201147739.921 01E+QB259701009283779.3.03E+082012201255107.001.27E-F0B300B300.9783259324E*08Dependent Variable: YMethod: Least SquaresDate; 06/04/14 Time; 14:24Sample: 1990 2012Included observations 23VariableCoefficientStd. Error(-StatisticProb.c1371799428.5462436778S00004

10、CZ-0,0001050000107-0.9770810.3415CK-0.0001629.39E-05-1.7241070.1016GY-0.0025500.001723d .4794380.1563TZ00002838 90E-053.1827910.0052R-squaredD995005Mean dependentvar16165.36Adjusted R-squared0.993895S.D. dependentvar150S7 01S E of regression1178 800Akaike info criterion17.17204Sum squared resid25012

11、248Schwarz criterion17.41889Log likelihood1924785Hannan-Qui nn enter.17.23412F-statistic896 42&6Durbin-Watson stat0.450074Prob(F-statistic)O.ODOOOO(图1 )如图所示分析结果可以看出：可绝系数高，修正的可决系数也高，表明模型拟合较好F值为 896.4256。K=4 n=23 n-K-1=18取a =0.05 Fa （4,18）=2.93 所以通过了 F检验。说明所选取的这些变量都对福建省的国内生产总值有显著性影响。T检验分析：T0.025（ 18）=

12、2.1009由得出数据可以知道：在百分之五的显著性水平下，财政支出和固定资产投资对国内生产总值分别有显著影响。P值的分析：由图中的结果可以看出来只有CK的P值较大未通过检验需要进行修正，其它的变量都通过了检验。经济意义：GDP= 1871.799-0.000105CZ财政收入）-0.000162CK （出口总额）- -0.002550GY （工业生产总值）+ 0.000283 TZ （固定资产投资额） 说明财政收入每减少 0.000105个单位，GDP增加一个单位。出 口总额每减少0.000162个单位，GDP增加一个单位.工业生产总 值每减少0.002550个单位，GDP增加一个单位。固定资

13、产投资 每增加0.000283个单位，GDP增加一个单位。二.异方差性处理与分析：图形法：如下图分别作出四个解释变量和E2间的散点图（1）CK(2)CZ(3) TZ(4) GY帥 Qtrt 150,DIM- TOC o 1-5 h z 441.OM -eU M,0W-oo魁 ROO -HflIC.DIMI -cQO O 0 0 -1 耳 灯115.0 5.5 E.fl! fi.5 T.fl 7.5 B.D S.5 B.DLOG(ZGY由以上散点图看出图形分布较散乱，不能确定是否存在异方差 性，所以需要进一步的检验。用怀特检验方法：O File Edit Object View Proc Qui

14、ck Options Window HelpVieA-1 Proc | Objec 11 Print Name | Freeze | Estimate | Foretas tStatsResidsHeteroskedasticrty Test V;hiteF-statistic3.813292Prob F(14,e)0.0316Obs*R-sq uared20.00259Prob. Chi-Square(14)0.1301Scaled explained SS4.018317Prob. Chi-Square14)0.9954Test Equation:Dependent Variable: R

15、ESIDE Method: Least SquaresDate: 06/04/14 Time: 15:00Sample: 1990 2012Induced observations： 23VariableCoefficientStd Error(-StatisticProbC1865536.697580.72.6742940.0282CZ-0.6781470.350382-1.6500490.1375CZA2-6.51E-0A5.42E-03-11.20163202639CZ*CK1 72E-074,16E-070.4123540.6909CZ*GY-B.97E-071.53E-05-0.45

16、49400.6612CZ*TZ5.39E-085 33E*081.0099140.3421CK3.7577651JD793933916720.00S5CK怪1 5&E-07388E-t)70 4015020,6985CK*GY-738E-061 25E-05-D. 59084305709CK*TZ128E-075.73E-070 22381608285GY-5.6517705.014037-11271780.2923GYfl2-5.68E-061.28E-05-0 44217S0.6701GY*TZ129E-0S774E-071.66989S0.1335TZ0.0172610.2041310.

17、0645530.9347TZA2-354E-08302E-08-1 1720910.2749R-squared0.669677l.lean dep&ndenl j呂1087439.Adjusted R-squared0 641613S.D. dependent var900539 6S.E. of regression539142.1Aka ike info criterion29.48164Sum squared residZ33E+12Schwarz criterion30.22218Log likelihood-324,0309Hannan-Quinn alter2966786F-sta

18、tistic3.813292Durbln-Watson stat1.871508Prob(F-statistic)0.031554(图2)根据图二中的数据，知 R=0.869677 nR2=23*0.869677=20.002571同时对于样本为 23, n=4的卡方分布临界值为9.39. nR2大于卡方分布的值。所以拒绝原假设，表 明残差是异方差的，存在异方差性。经过对数变换法处理之后得到如下结果: 塾 EViews - Equation: UNTITLED Workfile UNTTTL:UntitledlAle Edit Object View Proc Quick Options Wi

19、ndow Help 聊抽Proc| Object| Print| NamE | F皀Eze| Estinate| Forecast Stats | Rmwids |Dependent Variable: LOG(Y) Method: Least Squares Date: 06/0414 Time: 15 14 Sample: 1990 2012 Included observations: 23VariableCoefficientStd. Errort-StatisticProb.LOGCK)0.03277600492280.6658130.5140LOG(CZ)02643130 1532

20、871.4420750.1665LOG(GY)-04955010185541-2.6705550.0156LOG(TZ)1 0467270 3112603.3628580.0035C*7.0351760.801880*8 8357050.0000R-squared0.993323Mean dependentvar9 224006Adjusted R-squared0.991S39S D dependent var1 076318S E. of regression0097233Akaike Info criterion-1.633744Sum squared resid0 170173Schw

21、arz criterion1.386898Log likelihood23.78806Hannan-Quinn criter.-1.571663F*statistic659.4269Durbin-Walson stat0.435685Prob(F-statistic)0.000000(图3)Log(Y)= 0.032776LOG(CK)+0.264313 LOG(CZ)-0.495501LOG(GY)+1.046727 LOG(TZ)-7.085176和初始方程比较，无论是拟合优度还是参数的t值都有显著的改 善。经过处理后，其可绝系数和修正可绝系数都非常高。F检验统计值也得到了改善，即通过了

22、F统计检验。序列相关性的检验与解决用DW检验方法：由图1的回归分析中可以看到 Durbi n-Watson stat=0.450074然后查Durbin-Watson表知此处的DW直小于其下线临界值1.08根据判定的区域知这时的随机误差项存在正的一阶自相关。自相关的修正：运用Cochrage-Orcutt 迭代法进行自相关修正得到如下的结果:口 File Edit Object riew Proc Quick Options Window HelpView | Proc| Object | Print| NameFreeze | Estimate |ForecastStats | Resids

23、Dependent Variable: YMethod: Least SquaresDate: 06/04/14 Time： 15:33Sample (adjusted): 19912012In eluded observations: 22 after adjustme ntsConvergence achieved after 13 iterationsVariableCoefficientStd Error(StatisticProb.C-13970 4047294 9402953S9Q.7715CZ0 000130502E-052 5811800.0201CK-6.64E-05392E

24、-05-16939280 1097GY-0 0003030.0005141 5624180 1377TZ0 000117333E-053 505940Q.Q029AR(1)1 0200650.05609313.18516Q.0000R-squared0 999512MEan dependent var1634670Adjusted R-squared0 999359S.D dependent var15075.51S E of regression3B1 6707Akaike inro criterion1495399Sum squared resid2330760.Schwarz crite

25、rion1525155Log likelihood-1584939Han nmn-Quinn criter.1502409F-statistic6549.435Durbin-Watson stat1.520472Prob(F-statistic)OOOOOODInverted AR Roots1.02Estimated AR process is nonstatian白ry(图4)从该表可以看出，这时DW为1.520472。序列相关性得到了很好的修正。修正后：Y=0.000130CZ+(-6.64E-05)CK0.000803G Y+0.000117TZ-13970.40多重共线性的检验与解决

26、各解释变量的相关性系数很高，贝U存在多重共线性的问题。其解决办法为：利用逐步回归的方法，最后确定最适合的多元回归模型。分别将被解释变量与解释变量做简单的回归。下图为各解释变量之间的相关系数: tv lews * Urcup; UNIJILtD Workhle; UN 111 LbLA;UntitldOl File Edit Obje-dt View Proc Qu ick Options Window Help Vie輿 | Pcc | Object | Pnrrtj N日fde |FrEEze| EarnplE | 匀iE!Et | EtaE年ei:|CorrelationCZCKQYTZY

27、1.00000.9097170.7539260.9919570.996750GZ0 0697171.0000000.7026930J795190.989594CK07539260.7026931 0000000 7813900 775152CY0.9919570.9795190 7B13901 0000000.997022TZ0.9967500.9895940.7751520J9702211.000000務 EViiev;s - Equation: UNTULED Workfile: UNTITLED:Untitled口 File Edit Object View Proc Quick Opt

28、ions Window HelpViE训 | Proc| 出弐寸 Print|NafnE| 幵已己绘 | EstimmtE| Fortast Stats | Rmsids |Dependent Variable: YMethod Least SquaresDate: 06/04/14 Time: 15:44Sample: 1990 2012Included obseivations: 23VariableCoefficientStd. Errort-StatisticProb.C1371799428.54624.36778&0 0004CZ-0.0001050.000107-0.9770810

29、.3415CK-0.0001629.39E-05-17241070.1018GY-00025500001723-1.47943G0 1563TZ0.000283S.90E-053.1B27910.0052R-squared0995005Mean dependent var16165.36Adjusted R-squared0.993895S D dependentvar15087 01S.E. of regression1170.800Akaike info criterion17 172Q4Sum squared resid25012248Schwarz criterion17.41889L

30、og likelihood-192.4785Hannan-Quinn criter.17.23412F-statistlc8964256Durbin-Watson stat0 450074ProtKF-stahstic)0000000由图可知，解释变量之间存在高度的线性相关。由上表也可以看出尽管整体上的线性回归拟合较好，但CK（出口总额）和GY（工 业总产值）以及 CZ （财政收入）的系数符号与经济意义相悖 这表明模型中解释变量确实存在严重的多重共线性。多重共线性的修正：逐步回归法（1）运用OLS方法逐一求y对各个解释变量的回归。结合经济意义和统计检验选出拟合效果最好的一元线性回归方程。Y与C

31、K（出口）爾 EViews - (Equation: UNTITLED Workfile: UN7TTLED:；Untitled口 File Edit Object View Proc Quick Options Window Help 也ProcObject| Print|血ne|Fr亡诳| Estimate|Foreta5t|StatsResidsDependent Variable: YMethod: Least SquaresDate: 06/04/14 Time: 15:49Sample: 1990 2012Included observdions: 23VariableCoeffi

32、cientStd. Errort-StatsticProb.CK0.0016220.000308525895200000C53017928940.0874R-squared0.568404Mean dependent var16165.36Adjusted R-squared0.547351S D.dependent var150B701S E of regression1014481Aka ike info criterion21.37025Sum squared resid2.16E+09Schwarz citeri an21.46899Log likelihoo

33、d-243.7579Hannan-Quinn enter.21.39509F-statistic27 65658Durbin-Watson stat0 923503Prob(F-statistic)0.000033丫与CZ(财政收入)岳 EViews - (Equation: UNTITLED Workfile: UNTTTLED:UntitledO File Edit Object View Proc Quick Options Window Help；ieA : P-acioij5ct!戸5】屮加疋|日旳昂 Esdrite | Fore:sst Desies：Dependent Varia

34、ble: YMethod Least SquaresDate 06/04/14 Time: 15:50Sample: 1990 2012Included observations: 23VariableCoefficientStd. Errort-StatisticProV.CZ0.0004311.36E-0531.70709Q.0000C3938.898600.68826 5573090.0000R-squared0.979539Mean dependentvar16165.36Ad jsted R-squared0.978565S.D. dependentvar15087.01S E of

35、 regression2208.B64Aka ike info criterion1832129Sum squared resid1.02E+0SSchwarz criterion18.42003Log likelihood-208.694SHannan-Quinn criter.18.34612F-statisiic1Q05.340Durbin-Watson stat0.234006PrQh(F-statistic)0000000丫与GY （工业总产值） 胡 EViews - Equation: UN 111 LED Workfile: LJNTITLED：!Untitled) 口 File

36、 Edit Object View Proc Quick Options Window Help Vie悯fjrgcObject frintName斤eeze EstimateForecastStats ResidsDependent Variable: YMethod Least SquaresDate: 06/04/14 Time: 15:53Sample: 1990 2012Included observations: 23VariableCoefficientStd ErrorVStatisticProb.GY0.0051910.00014535.912450.0000C1232239

37、582.25132.1163360.0464R-squared0933978Mean dependentvar16165.36Adjusted R-squared0.983215S D. dependent var15037.01S E of regression1954 519Aka ike info criterion18.07672Sum squared restd80231268Schwarz criterion18.17546Leg EiKeHhood知5.8823Hannan-Quinn criter,1&.1015Sstatistic12&9.70 斗Durbin-Watson

38、stat(J.9&499QProb(F-statistic)0000000Y与TZ （固定资产投资）監1 EVievzs - (Equation: UNTITLED WorkfiJe: UN TITLED: ;Untitled 匚3 File Edit Object View Proc Quick Options Window Hep ViewJ Proc|Qq亡训 Print 曲蛙 |Fiee刽 Estimate Fornea就5tat刮 只亡skfc|DependentVariatle: Y Method Least SquaresDate: 06/04714 Time: 15:00Sam

39、ple: 1990 2012Included observations: 23VariableCoefficientStd Errort-StatisticProbGY-0.0016070.001168-12751040.1843TZ0.0002093.56E-D55,8353050.0000C1976.2423B4 67165 1374790.0001R-squared0994072Mean dependEnt var16165.36Adjusted R-squared0.993479S.D. dependentvar15087,01S.E. of regression1218347Akai

40、ke Info criterion17 16943Sum squared resid296S7367Schwarz criterE on1731759Log likelihood-194.4491Hannan-Quinn enter.17 20673F-statistic1676.775Durbin-Watson stat0.725299Pro b(F-stati stic)0.000000爾 EViev.s - Equation: UNTITLED Workfile: UNTrTLED:Urtitled3 File Edit Object View Proc Quick Options Wi

41、ndow Help yiewProc|oKt|Name | Freeze | E前mBte|Faretast| S回 RedsjDependentVariable: YMethod Least SquaresDate: 06704/14 Time: 15:54Sample: 1990 2012Included obGervatio ns: 23VariableCoefficientStd. Errort-StatisticProb.TZ0.0001592.60E*0656.703230.0000C1773.954362.89294.8883690.0001R-squared0993511Mean dependent var16165.36Adjusted R-squared0993202S D. dependentvar150B701S E. of regresston1243 922Akaike info criterion17.17237Sum squared resid32494175Schwancntenon17.27161Log likeEihood-195.4880Hannan-Quinn 匚riter.17.19770F-statistic3215.256Dur bin-Watson stat0.020153Prab(F-s