中国进出口贸易现状分析

1、-. z. . - . -可修- .目录 TOC o 1-3 u 摘要= 1 * ROMANIAbstract= 1 * ROMANI1.导论.12.数据处理与统计分析.1 2.1数据样本与变量指标.1 2.1.1数据来源.12.1.2数据处理.2 2.2统计分析.2 2.2.1相关分析.2 2.2.2初步OLS回归.3 2.2.3平稳性检验.5 2.2.4协整检验.10 2.2.5 ECM误差修正模型.11 2.2.6对回归模型的检查.11 2.2.7迭代估计法.13 2.2.8预测.143.结论.164.政策建议.16参考文献：16摘 要根据1978年2012年我国GDP和进出口贸易的相关

2、数据,本文运用协整理论和ECM误差修正模型、迭代估计法相关知识,对我国GDP和进出口贸易的关系进展检验。结果说明,1978年2012年,我国GDP和进出口贸易的相关数据之间存在长期稳定的均衡关系;通过对GDP和进出口关系的模型的估计可以看出进出口总额的增长都会带来GDP的增长，而且进口对GDP增长的解释能力较强;并且对GDP进展了2013和2014年的回归预测。因此我国应适度扩大进口,改善出口产品构造，提高出口产品质量水平,构建核心竞争力,以减少出口受国外经济环境变化而影响GDP增长的稳定。关键词：GDP；进出口；协整理论；ECM误差修正模型；AbstractAccording to the

3、1978 2012 Chinas GDP and import and e*port trade related data, by using the theory of Cointegration 、correction model of error ECM、 modified iterative estimation model, to test the relationship of GDP and the import and e*port trade. The results show that, from 1978 to 2012, there is a long-term sta

4、ble equilibrium relationship between Chinas GDP and the related data of import and e*port trade; by estimating the GDP and import & E*port relationship model can be seen in the total import and e*port volume growth will bring about the growth of GDP, and the importhas stronger ability to e*plain the

5、 growth of GDP; and the regression forecast of 2013 and 2014. Therefore, our country should be appropriate to e*pand imports, improve the structure of e*port products, improve the e*port product quality level, to build the core petitiveness, to reduce the impact of foreign economic environment chang

6、es on the growth of GDP stability.Key Words：GDP; import and e*port; cointegration theory ;correction model of error ECM;= 1 * ROMANI-. z.-. z.导论1978年以来，中国的对外经济发生了翻天覆地的变化，对外贸易成为了国民经济增长的重要推动力。目前国际上衡量一个经济大国有两条通用的硬性标准，一是年度国生产总值超过10000亿美元，二是进出口总额超过5000亿美元。由此不难看出，进出口总额充分反映了一个国家或者地区参与世界经济的程度，无论是从世界围来看，还是从中

7、国本身经历过的历史来看，将不难发现对外开放程度是一国经济水平的决定因素。因此有必要对进出口与GDP增长的关系作出定量统计分析。论文容数据样本与变量指标数据来源：选取1978-2012年的进口和出口额作为反映中国进出口情况的统计量，选取1978-2012年的GDP作为反映中国的经济增长的统计量，数据均来自中国统计年鉴 。将原数据导入到E*cel，如图2.1.1：数据的处理将原始数据中的变量进口额命名为I，出口额为E，为消除物价因素对进出口额的影响，使用商品零售价格指数以1978年为不变价格对进出口额分别进展处理(用原变量I、E分别除以相应年份的以1978年为不变价格的商品零售价格指数)，修正后的

8、变量名分别为I_adjust，E_adjust；使用居民消费价格指数以1978年为不变价格对国生产总值GDP进展处理用原GDP除以以1978年为不变价格的居民消费价格指数，得到真实的GDP值，修正后的变量名为GDP_adjust。如图2.1.2：统计分析过程相关分析通过观察进口、出口、GDP的散点图图2.2.1和相关系数矩阵图2.2.2，初步可知它们之间存在着较强的相关关系。初步OLS回归估计Dependent Variable: GDP_ADJUSTMethod: Least SquaresDate: 03/14/14 Time: 10:41Sample: 1978 2012Included

9、 observations: 35VariableCoefficientStd. Errort-StatisticProb.C4886.207939.21735.2024250.0000I_ADJUST4.2963111.0953683.9222520.0004E_ADJUST-1.2090160.936483-1.2910160.2059R-squared0.972023Mean dependent var24934.71Adjusted R-squared0.970275S.D. dependent var24418.02S.E. of regression4209.905Akaike i

10、nfo criterion19.61008Sum squared resid5.67E+08Schwarz criterion19.74340Log likelihood-340.1765F-statistic555.9066Durbin-Watson stat0.692496Prob(F-statistic)0.000000通过Eviews软件分析,结果如上表2.2.1，对GDP与进出口的模型进展了估计，估计的回归模型为：GDP_adjust=4886.207+4.296311*I_adjust-1.209016*E_adjust SE: (939.2173) (1.095368) (0.9

11、36483)t-statistic: (5.202425) (3.922252) (-1.291016)模型的拟合优度R2=0.972023，调整后的R2=0.970275，回归系数的t检验在=0.05的显著性水平下显著，F=555.9066，在=0.05的显著性水平下显著Prob(F-statistic)0.05。DW=0.692，在=0.05的显著性水平下，查DW检验表可知，dl=1.34,du=1.58,4-du=2.42,4- dl=2.66，0DW dl,序列存在正自相关。进而进展怀特检验以检验是否存在异方差性：White Heteroskedasticity Test:F-stat

12、istic6.972502Probability0.000430Obs*R-squared16.86215Probability0.002056Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 03/14/14 Time: 10:47Sample: 1978 2012Included observations: 35VariableCoefficientStd. Errort-StatisticProb.C-5740439.6525670.-0.8796700.3860I_ADJUST-23897.862071

13、5.10-1.1536450.2578I_ADJUST20.6299690.5259461.1977840.2404E_ADJUST28511.1518331.371.5553200.1304E_ADJUST2-0.7154430.420545-1.7012290.0992R-squared0.481776Mean dependent var16204160Adjusted R-squared0.412679S.D. dependent var29477994S.E. of regression22591007Akaike info criterion36.83557Sum squared r

14、esid1.53E+16Schwarz criterion37.05776Log likelihood-639.6224F-statistic6.972502Durbin-Watson stat2.123779Prob(F-statistic)0.000430如表2.2.2，根据怀特检验，F-statistic=6.972502，在=0.05的显著性水平下,方程整体显著P=0.00043, Obs*R-square=16.86215，在=0.05的显著性水平下，P=0.002050.05，无法拒绝原假设，lngdp_adjust有单位根，可认为该序列非平稳，继续进展ADF检验，最后一阶差分模型

15、= 3 * ROMANIII通过检验。见图2.2.8一阶差分的ADF检验结果：Augmented Dickey-Fuller Test EquationDependent Variable: D(LNGDP_ADJUST,2)VariableCoefficientStd. Errort-StatisticProb.D(LNGDP_ADJUST(-1)-0.7340660.180811-4.0598630.0004D(LNGDP_ADJUST(-1),2)0.4067450.1730832.3499980.0261C0.0563790.0172563.2671770.0029TREND(1978

16、)0.0007590.0006961.0906050.2847R-squared0.380149Mean dependent var0.000856Adjusted R-squared0.313737S.D. dependent var0.039357S.E. of regression0.032603Akaike info criterion-3.892327Sum squared resid0.029764Schwarz criterion-3.709110Log likelihood66.27723F-statistic5.724057Durbin-Watson stat1.982486

17、Prob(F-statistic)0.003474由表2.2.3可知在=0.05的显著性水平下,统计量t=-4.059863,P远小于，不存在单位根，lngdp_adjust的一阶差分序列平稳，为一阶单整序列I(1)。同理可得，lni_adjust的一阶差分序列是平稳的，即进口序列是一阶单整的见表2.2.4。lne_adjust的一阶差分序列是平稳的，及出口序列是一阶单整的见表2.2.5。进口的ADF检验：Null Hypothesis: D(LNI_ADJUST) has a unit rootE*ogenous: Constant, Linear TrendLag Length: 0 (A

18、utomatic based on SIC, MA*LAG=12)t-StatisticProb.*Augmented Dickey-Fuller test statistic-4.2522530.0103Test critical values:1% level-4.2627355% level-3.55297310% level-3.209642*MacKinnon (1996) one-sided p-values.Augmented Dickey-Fuller Test EquationDependent Variable: D(LNI_ADJUST,2)Method: Least S

19、quaresDate: 03/14/14 Time: 11:16Sample (adjusted): 1980 2012Included observations: 33 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.D(LNI_ADJUST(-1)-0.7592520.178553-4.2522530.0002C0.1358810.0658172.0645400.0477TREND(1978)-0.0016300.002832-0.5754600.5693R-squared0.376232Mean dependen

20、t var-0.007423Adjusted R-squared0.334648S.D. dependent var0.188782S.E. of regression0.153988Akaike info criterion-0.817374Sum squared resid0.711371Schwarz criterion-0.681327Log likelihood16.48666F-statistic9.047405Durbin-Watson stat1.853212Prob(F-statistic)0.000842出口的ADF检验：Null Hypothesis: D(LNE_ADJ

21、UST) has a unit rootE*ogenous: Constant, Linear TrendLag Length: 0 (Automatic based on SIC, MA*LAG=12)t-StatisticProb.*Augmented Dickey-Fuller test statistic-5.8301620.0002Test critical values:1% level-4.2627355% level-3.55297310% level-3.209642*MacKinnon (1996) one-sided p-values.Augmented Dickey-F

22、uller Test EquationDependent Variable: D(LNE_ADJUST,2)Method: Least SquaresDate: 03/14/14 Time: 11:17Sample (adjusted): 1980 2012Included observations: 33 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.D(LNE_ADJUST(-1)-1.0677590.183144-5.8301620.0000C0.2122250.0642243.3044550.0025TREN

23、D(1978)-0.0028430.002638-1.0780280.2896R-squared0.531317Mean dependent var-0.005608Adjusted R-squared0.500072S.D. dependent var0.201351S.E. of regression0.142367Akaike info criterion-0.974315Sum squared resid0.608047Schwarz criterion-0.838269Log likelihood19.07619F-statistic17.00460Durbin-Watson sta

24、t2.020752Prob(F-statistic)0.000012协整性检验Johansen协整检验：Unrestricted Cointegration Rank Test (Trace)HypothesizedTrace0.05No. of CE(s)EigenvalueStatisticCritical ValueProb.*None *0.46039330.2347829.797070.0445At most 10.25058519.87660015.494710.0293At most 20.0107710.3573563.8414660.5500Trace test indica

25、tes 1 cointegrating eqn(s) at the 0.05 level* denotes rejection of the hypothesis at the 0.05 level*MacKinnon-Haug-Michelis (1999) p-values根据表2.2.6，检验判定：原假设None表示没有协整关系，该假设下计算的迹统计量值为30.23478，大于临界值29.79707且P值为0.0445，在=0.05的显著性水平下，可以拒绝原假设，认为至少存在一个协整关系；下一个原假设AT most 1表示最多有一个协整关系，该假设下计算的迹统计量值为19.876600，

26、小于临界值15.49471且P值0.0293，在=0.05的显著性水平下，拒绝原假设，认为至少存在两个协整关系。下一个原假设AT most 2表示最多有两个协整关系，该假设下计算的迹统计量值为0.357356，小于临界值3.841466且P值0.5500，在=0.05的显著性水平下，无法拒绝原假设，认为存在两个协整关系。用OLS法对lngdp_adjustt=c+*lni_adjust+*lne_adjust+ut进展估计，如表2.2.7： 表2.2.,7Dependent Variable: LNGDP_ADJUSTMethod: Least SquaresDate: 03/14/14 Ti

27、me: 11:24Sample: 1978 2012Included observations: 35VariableCoefficientStd. Errort-StatisticProb.C4.8146390.15115231.852880.0000LNI_ADJUST0.2611140.1952311.3374600.1905LNE_ADJUST0.3503640.1837441.9068080.0656R-squared0.979828Mean dependent var9.677309Adjusted R-squared0.978567S.D. dependent var0.9692

28、04S.E. of regression0.141891Akaike info criterion-0.985703Sum squared resid0.644255Schwarz criterion-0.852388Log likelihood20.24980F-statistic777.1788Durbin-Watson stat0.277642Prob(F-statistic)0.000000协整回归方程为：LNGDP_ADJUST=4.814639+0.261114* LNI_ADJUST+0.350364* LNE_ADJUSTSE： 0.151152 0.195231 0.1837

29、44T: (31.85288) 1.337460 1.906808 R2=0.979828 F=777.1788对上述协整回归方程做怀特检验的结果：White Heteroskedasticity Test:F-statistic6.138925Probability0.000985Obs*R-squared15.75362Probability0.003368如表2.2.8，在的显著性水平下，F-statistic=6.13892，P值=0.00远小于，模型显著。 Obs*R-squared=15.7536，P值=0.003，拒绝原假设，方程存在异方差。因此建立CM误差修正模型消除异方差。E

30、CM误差修正模型Dependent Variable: D(LNGDP_ADJUST)VariableCoefficientStd. Errort-StatisticProb.C-0.0601400.044884-1.3398790.1903D(LNI_ADJUST)0.1700840.0472083.6028550.0011D(LNE_ADJUST)-0.0181330.052569-0.3449480.7325ECM0.0091590.0029563.0981800.0042R-squared0.461612Mean dependent var0.094178Adjusted R-squa

31、red0.407773S.D. dependent var0.040229S.E. of regression0.030959Akaike info criterion-4.002196Sum squared resid0.028753Schwarz criterion-3.822624Log likelihood72.03733F-statistic8.573952Durbin-Watson stat1.360958Prob(F-statistic)0.000292根据表2.2.9：ECM形式：Estimation Equation:=D(LNGDP_ADJUST) = C(1) + C(2

32、)*D(LNI_ADJUST) + C(3)*D(LNE_ADJUST) + C(4)*ECMSubstituted Coefficients:=D(LNGDP_ADJUST) = -0. + 0.1700839636*D(LNI_ADJUST) -0.*D(LNE_ADJUST) + 0.8*ECM对回归模型的检查基于上述协整检验所得的模型：通过异方差的white检验，见下表2.2.10：White Heteroskedasticity Test:F-statistic0.517303Probability0.789971Obs*R-squared3.505529Probability0.7

33、43234在显著性水平为0.05时，P值远大于，无法拒绝原假设，认为不存在异方差性。模型的拟合程度很高，而且模型Prob(F-statistic)远小于是显著的，但检验出出口变量的系数不显著，可能存在多重共线性。通过观察相关系数矩阵图2.2.9，进口与出口是中度线性相关的，所以当进出口同时解释GDP时，出口变得不显著。GDP对进口的ECM修正模型，见表2.2.11：Dependent Variable: D(LNGDP_ADJUST)VariableCoefficientStd. Errort-StatisticProb.C-0.0791820.049635-1.5952860.1208D(L

34、NI_ADJUST)0.1585950.0355014.4673380.0001ECM0.0128670.0041233.1209270.0039R-squared0.451019Mean dependent var0.094178Adjusted R-squared0.415600S.D. dependent var0.040229S.E. of regression0.030753Akaike info criterion-4.041535Sum squared resid0.029319Schwarz criterion-3.906856Log likelihood71.70609F-s

35、tatistic12.73410Durbin-Watson stat1.328390Prob(F-statistic)0.000092Substituted Coefficients:=D(LNGDP_ADJUST) = -0. + 0.1585947675*D(LNI_ADJUST) + 0.*ECMGDP对出口的ECM修正模型，见表2.2.12：Dependent Variable: D(LNGDP_ADJUST)VariableCoefficientStd. Errort-StatisticProb.C-0.0576130.056676-1.0165430.3172D(LNE_ADJUS

36、T)0.1060660.0468742.2627560.0308ECM0.0109430.0043792.4988420.0180R-squared0.230296Mean dependent var0.094178Adjusted R-squared0.180638S.D. dependent var0.040229S.E. of regression0.036415Akaike info criterion-3.703594Sum squared resid0.041107Schwarz criterion-3.568915Log likelihood65.96109F-statistic

37、4.637617Durbin-Watson stat1.089222Prob(F-statistic)0.017299Substituted Coefficients:=D(LNGDP_ADJUST) = -0. + 0.1060655395*D(LNE_ADJUST) + 0.*ECM由于存在多重共线性，且GDP对进口的模型数据拟合较好，但查阅资料和相关数据发现我国GDP的开展很大程度也取决于出口，因此，将GDP分别对进口和出口进展拟合，从各自的模型来分析GDP与进口、出口的关系。由于是时间序列，应该检验模型的自相关性。在的显著性水平下，GDP与进口模型的DW=1.33，与出口模型的DW=1

38、.089，查DW检验表可知，dl=1.4,du=1.52,4-du=2.48,4- dl=2.6，0DW dl ,两个模型均存在正自相关。运用广义差分法对模型进展修正。Cochrane-Orcutt迭代估计法GDP与进口的模型，见表2.2.13：Dependent Variable: D(LNGDP_ADJUST)VariableCoefficientStd. Errort-StatisticProb.C-0.0621920.080054-0.7768800.4435D(LNI_ADJUST)0.1328800.0362923.6613770.0010ECM0.0116500.0066601.

39、7493620.0908AR(1)0.3740390.1833802.0396980.0506R-squared0.517308Mean dependent var0.094317Adjusted R-squared0.467374S.D. dependent var0.040844S.E. of regression0.029809Akaike info criterion-4.074822Sum squared resid0.025768Schwarz criterion-3.893427Log likelihood71.23457F-statistic10.35991Durbin-Wat

40、son stat1.663102Prob(F-statistic)0.000084Inverted AR Roots0.37新的GDP与进口模型：D(LNGDP_ADJUST) = -0. + 0.1328801672*D(LNI_ADJUST) + 0.*ECM SE: 0.080054 0.036292 0.00666T: (-0.776880) (3.661377) (1.749362)R2=0.467374 F=10.35991在的显著性水平下，参数与模型都是显著的。通过怀特检验，模型不存在异方差。DW=1.663，查DW检验表可知，dl=1.4,du=1.52,4-du=2.48,4- dl=2.6，du DW4-du,消除了自相关性。Dependent Variable: D(LNGDP_ADJUST)VariableCoefficientStd. Errort-StatisticProb.C-0.0422080.100021-0.4219930.6761D(LNE_ADJUST)0.0835570.0380772.1944080.0364ECM0.0098230.0078261.2552020.2194