我國(guó)出口貿(mào)易影響因素的計(jì)量經(jīng)濟(jì)分析計(jì)量經(jīng)濟(jì)學(xué)論文

1、我國(guó)出口貿(mào)易影響因素的計(jì)量經(jīng) 濟(jì)分析我國(guó)出口貿(mào)易影響因素的計(jì)量經(jīng)濟(jì)分析摘要：本文收集了我國(guó)198-2011年出口額等時(shí)間序列統(tǒng)計(jì)資料，建立了我國(guó)出口總額的影響因素 模型，并對(duì)我國(guó)下期出口額進(jìn)行預(yù)測(cè)。在建立模型的過(guò)程中，處理了多重共線性問(wèn)題，修正了異方 差,避免了自相關(guān)等問(wèn)題模型結(jié)果表明， 影響我國(guó)出口貿(mào)易額的主要因素為國(guó)內(nèi)生產(chǎn)總值、全國(guó)固定資產(chǎn)投資及國(guó)民消費(fèi)水平。關(guān)鍵詞：出口貿(mào)易；影響因素分析；多重共線性；異方差；自相關(guān)；計(jì)量經(jīng)濟(jì)模型一、引言三十多年來(lái)，我國(guó)對(duì)外貿(mào)易取得了舉世矚目的成就，從一個(gè)較低的水平發(fā)展了一個(gè)很高的水平，中國(guó)經(jīng)濟(jì)在世界經(jīng)濟(jì)中扮演的角色也越來(lái)越重要，越來(lái)越受世界各國(guó)的關(guān)注。因

2、此，對(duì)外貿(mào)經(jīng)濟(jì)的分析越發(fā)重要。從目前的理論的研究來(lái)看，影響我國(guó)出口發(fā)展的因素國(guó)內(nèi)生產(chǎn)總值、全國(guó)固定資產(chǎn)投資、居民 消費(fèi)水平、關(guān)稅、城鄉(xiāng)居民儲(chǔ)蓄、第三產(chǎn)業(yè)就業(yè)人數(shù)。本文在前人分析的基礎(chǔ)上，結(jié)合計(jì)量分析方 法,分別建立了我國(guó)出口的影響因素模型，來(lái)分析研究各個(gè)因素對(duì)我國(guó)進(jìn)出口貿(mào)易的影響方向和力度。二、理論模型與數(shù)據(jù)（1）GDP（XI）國(guó)民總收入體現(xiàn)了一國(guó)整體發(fā)展水平，經(jīng)濟(jì)發(fā)展?fàn)顩r不同，對(duì)外貿(mào)易情 況受到的影響也就不同。（2）全國(guó)固定資產(chǎn)投資（X2）固定資產(chǎn)投資反映了國(guó)內(nèi)環(huán)境因素的變量，間接對(duì)我國(guó)進(jìn) 出口貿(mào)易也產(chǎn)生了一定的影響。（3）居民消費(fèi)水平（X3）-高的物價(jià)將會(huì)導(dǎo)致出口商品成本上升，對(duì)我國(guó)出 口

3、一般情況下，會(huì)有反向影響的作用。（4）關(guān)稅（X4）-進(jìn)口關(guān)稅稅率是調(diào)節(jié)進(jìn)口商品數(shù)量和結(jié)構(gòu)的重要手段，較高稅 率一般情況下會(huì)導(dǎo)致進(jìn)口數(shù)量的減少。（5） 城鄉(xiāng)居民儲(chǔ)蓄（X5）-居民儲(chǔ)蓄對(duì)從另外的角度又反映了一國(guó)經(jīng)濟(jì)發(fā)展?fàn)顩r。而一過(guò)進(jìn)出 口貿(mào)易又很大程度上依賴(lài)于這個(gè)國(guó)家的經(jīng)濟(jì)發(fā)展水平。（6）第三產(chǎn)業(yè)就業(yè)人數(shù)（X6）-第三產(chǎn)業(yè)服務(wù)業(yè)比重對(duì)我國(guó)進(jìn)出口貿(mào)易也有不可忽視的重要影 響。一般服務(wù)不出國(guó)，所以第三產(chǎn)業(yè)比重越高，進(jìn)出口總額總額在經(jīng)濟(jì)總量中的比重就會(huì)降低。三、模型構(gòu)建經(jīng)分析國(guó)內(nèi)生產(chǎn)總值及全國(guó)固定資產(chǎn)投資為影響我國(guó)出口貿(mào)易的主要因素，除此之外，城鄉(xiāng)居 民消費(fèi)、關(guān)稅、城鄉(xiāng)居民儲(chǔ)蓄和第三產(chǎn)業(yè)的工作人數(shù)等也有

4、關(guān)系。為此，考慮的影響因數(shù)主要有全國(guó)生產(chǎn)總值X1，全國(guó)固定資產(chǎn)投資X2,我國(guó)居民的消費(fèi)狀況 X3,我國(guó)海關(guān)關(guān)稅情況X4,居民的 儲(chǔ)蓄情況X5,第三產(chǎn)業(yè)發(fā)展情況X&為此設(shè)定了如下形式的計(jì)量經(jīng)濟(jì)模型：Y t=B o + B 1 X1 + B 2X2+3 3X3 + B 4X4+ B 5X5+ B 6X6+ 卩 t其中，Y為我國(guó)出口總額，GDP X1全國(guó)固定資產(chǎn)投資X2,居民消費(fèi)水平X3,關(guān)稅X4,城鄉(xiāng)居 民儲(chǔ)蓄X5,第三產(chǎn)業(yè)就業(yè)人數(shù)X6利用EViews軟件，生成Y、X1、X2、X3 X4 X5 X6等數(shù)據(jù)，采用這些數(shù)據(jù)對(duì)模型進(jìn)行 OLS 回歸，記過(guò)如下表所示：為估計(jì)模型參數(shù)，搜集了 1982-20

5、11的統(tǒng)計(jì)數(shù)據(jù)，如表1所示：全國(guó)固定資 居民消城鄉(xiāng)居民儲(chǔ) 第三產(chǎn)業(yè)出口總額 GDP產(chǎn)投資費(fèi)水平關(guān)稅蓄就業(yè)人數(shù)1982413.85,323.351,230.4028847.46447.36,090.001983438.35,962.651,430.1031653.88572.66,606.001984580.57,208.051,832.90361103.07776.627,739.001985808.99,016.042,543.20446205.211,622.608,359.0019861,082.1010,275.183,120.60497151.621,471.458,811.0019

6、871,470.0012,058.623,791.70565142.372,067.609,395.0019881,766.7015,042.824,753.80714155.022,659.169,933.0019891,956.1016,992.324,410.40788181.545,196.4010,129.0019902,985.8018,667.824,517.00833159.017,119.6011,979.0019913,827.1021,781.505,594.50932187.289,244.9012,378.0019924,676.3026,923.488,080.10

7、1,116212.7511,757.3013,098.0019935,284.8035,333.9213,072.301,393256.4715,203.5014,163.00199410,421.8048,197.8617,042.101,833272.6821,518.8015,515.00199512,451.8060,793.7320,019.302,355291.8329,662.3016,880.00199612,576.4071,176.5922,913.502,789301.8438,520.8017,927.00199715,160.7078,973.0324,941.103

8、,002319.4946,279.8018,432.00199815,223.6084,402.2828,406.203,159313.0453,407.4718,860.00199916,159.8089,677.0529,854.703,346562.2359,621.8319,205.00200020,634.4099,214.5532,917.703,632750.4864,332.3819,823.40200122,024.40109,655.1737,213.503,887840.5273,762.4320,164.80200226,947.90120,332.6943,499.9

9、04,144704.2786,910.6520,958.10200336,287.90135,822.7655,566.614,475923.13103,617.6521,604.60200449,103.30159,878.3470,477.435,0321,043.77119,555.3922,724.80200562,648.10184,937.3788,773.615,5961,066.17141,050.9923,439.20200677,597.20216,314.43109,998.166,2991,141.78161,587.3024,142.90200793,563.6026

10、5,810.31137,323.947,3101,432.57172,534.1924,404.002008100,394.94314,045.43172,828.408,4301,769.95217,885.3525,087.20200982,029.69340,902.81224,598.779,2831,483.81260,771.6625,857.302010107,022.84401,512.80251,683.7710,5222,027.83303,302.4926,332.302011123,240.60473,104.05311,485.1312,5702,559.12343,

11、635.8927,282.00數(shù)據(jù)來(lái)源：國(guó)家統(tǒng)計(jì)局網(wǎng)站 利用EViews軟件，生成Y、 記過(guò)如下表2所示：XI、X2、X3 X4 X5 X6等數(shù)據(jù),采用這些數(shù)據(jù)對(duì)模型進(jìn)行OLS回歸,Dependent Variable: YMethod: Least SquaresDate: 12/17/13 Time: 09:25Sample: 1982 2011Included observations: 30VariableCoefficientStd. Errort-StatisticProb.C-3685.4086155.206-0.5987460.5552X11.6000660.1940958.2

12、437380.0000X2-0.7255720.146212-4.9624490.0001X3-35.872916.403180-5.6023580.0000X4-0.3806668.431707-0.0451470.9644X50.0530700.1251020.4242130.6754X60.8990610.8045541.1174650.2753R-squared0.989683Mean dependent var30292.65Adjusted R-squared0.986991S.D. dependent var37699.18S.E. of regression4299.828Ak

13、aike info criterion19.77150Sum squared resid4.25E+08Schwarz criterion20.09845Log likelihood-289.5725Hannan-Quinn criter.19.87609F-statistic367.7091Durbin-Watson stat1.363408Prob(F-statistic)0.0000002由此可見(jiàn)該模型 R2 =0.989683, R =0.986991，可決系數(shù)很高，F(xiàn)檢驗(yàn)值367.7091，明顯顯著。但是當(dāng)a =0.05時(shí)，ta/2 (n-k ) =t0.025(30-6)=2.06

14、4,不僅X4 X5、X6的系數(shù)t檢驗(yàn)不顯著，這表明可 能存在嚴(yán)重的多重共線性。計(jì)算各解釋變量的相關(guān)系數(shù)，得相關(guān)系數(shù)矩陣：表3相關(guān)系數(shù)矩陣由相關(guān)系數(shù)矩陣可以看出，各解釋變量相互之間的相關(guān)系數(shù)較高，證實(shí)確實(shí)存在嚴(yán)重多重共線 性。四、修正多重共線性采用逐步回歸的辦法，去檢驗(yàn)和解決多重共線性問(wèn)題。分別作Y寸X1、X2、X3 X4 X5 X6的一元回歸，結(jié)果如下：表4一元回歸估計(jì)結(jié)果變量x1 x2 x3 x4 x5 x6參數(shù)估計(jì)值0.2890300.43883911.0069156.190430.3806404.877271T統(tǒng)計(jì)變量236.0545917.2557521.7063723.1900923

15、.179018.6928622R20.9603870.914047 0.9439060.9505110.9504660.7296412R0.9589720.9109780.9419030.9487430.9486970.7199852其中，加入X1的方程R 最大，以X1為基礎(chǔ)，順次加入其它變量逐步回歸。結(jié)果如下表5所示X1X2X3X4X5X6變量X1,x20.412851(6.284981)-0.195267(-2.910023)0.962517X1,x0.397389-0.418300.9588903(3.54538275)(-0.971530)X1,x0.19635618.377720.9

16、606104(3.07176(1.471197)7)X1,x0.451806-0.216010.9590515(2.8425821)(-1.026622)X1,x0.2842830.1060160.9575456(12.5568(0.241879)9)2經(jīng)比較，新加入X2的方程R =0.962517，改進(jìn)最大，而且各參數(shù)的t檢驗(yàn)顯著，選擇保留X2, 再加入其它新變量逐步回歸，結(jié)果如下所示：表6加入新變量的回歸結(jié)果變量 變量X1X2X3X4X5X6X1,x2,1.559483-0.789531-29.716810.9876x3(9.89800(-8.00779(-7.49482843)7)7)X

17、1,x2,0.332469-0.16092511.621740.9622x4(2.97114(-1.46750(0.88914242)7)6)X1,x2,0.509013-0.180063-0.1404060.9617x5(3.23689(-1.70333(-0.67451455)2)3)X1,x2,0.841019-0.703960-2.2582130.9726x6(5.97596(-3.98934(-3.31747524)9)0)2經(jīng)比較，新加入X3的方程R =0.987684，改進(jìn)最大，而且各參數(shù)的t檢驗(yàn)顯著，選擇保留X3, 再加入其它新變量逐步回歸，結(jié)果如下所示：表7加入新變量的回歸結(jié)

18、果變量X1X2X3X4X5X6X1,x2,x31.59640-0.806762-30.1316-3.023880.9872,x45(-7.348235267(8.54989)(-7.2204(-0.384999)49)65)X1,x2,x31.53790-0.803017-30.10280.053240.9872,x59(-7.6552906987(9.1738)(-7.2963(0.433231)16)78)X1,x2,x31.61777-0.708772-35.52140.912500.9879,x68(-6.04792951438(9.936624)(-5.826707)(1.24411

19、8)2 2在XI、X2、X3勺基礎(chǔ)上加入后的方程 R 有所下降，且X4參數(shù)的t檢驗(yàn)不顯著。加入X5時(shí)，R2也有所下降，且X5參數(shù)的t檢驗(yàn)不顯著。加入X6后, R 也有下降，X6參數(shù)的t檢驗(yàn)不顯著。從相 關(guān)系數(shù)也可以看出，X4 X5 X6與其他變量高度相關(guān)，這說(shuō)明主要是X4 X5 X6引起了多重共線性， 予以剔除。最后修正嚴(yán)重多重共線性影響后的回歸結(jié)果為=2052.388+1.559483X1-0.789531X2-29.71681X3(1.201916) (9.898003) (-8.007797) (-7.494827)2R2 =0.988958R =0.987684 F=776.2257

20、DW=1.254051 SE=4183.76五、異方差檢驗(yàn)及修正(一)檢驗(yàn)異方差 根據(jù)以上結(jié)果，進(jìn)行White檢驗(yàn)，得表8Heteroskedasticity Test: WhiteF-statistic7.129311Prob.F(9,20)0.0001Obs*R-squared22.87104Prob.Chi-Square(9)0.0065Scaled explained SS41.07136Prob.Chi-Square(9)0.0000Test Equation:Dependent Variable: RESIDA2Method: Least SquaresDate: 12/17/13

21、 Time: 11:44Sample: 1982 2011Included observations: 30VariableCoefficientStd. Errort-StatisticProb.C-3059484.31902139-0.0959020.9246X1-3025.14515128.33-0.1999660.8435X1A2-0.5969730.182148-3.2773990.0038X1*X20.8454610.1429475.9144990.0000X1*X325.0484613.032571.9219900.0690X21978.59610384.790.1905280.

22、8508X2a2-0.3533920.091941-3.8436650.0010X2*X3-15.906765.457769-2.9145170.0086X365275.13323426.60.2018240.8421X3A2-274.4179197.9826-1.3860710.1810R-squared0.762368Mean dependent var15169897Adjusted R-squared0.655434S.D. dependent var33739116S.E. of regression19804781Akaike info criterion36.70195Sum s

23、quared resid7.84E+15Schwarz criterion37.16901Log likelihood-540.5292Hannan-Quinn criter.36.85136F-statistic7.129311Durbin-Watson stat1.498529Prob(F-statistic)0.000134從表8可知n R2 =22.87104，有 White檢驗(yàn)知，在a =0.05下，查X2分布表，得臨界值 X：o5（9）=16.919O，同時(shí)X和X2的t檢驗(yàn)值也顯著。比較計(jì)算的 X2的統(tǒng)計(jì)量與臨界值，因?yàn)?n R2 =22.87104X：05（9）=16.9190,

24、所以拒絕原假設(shè)，不拒絕備擇假設(shè)，表明模型存在異方差。（二）異方差性修正在運(yùn)用加權(quán)最小二乘法估計(jì)的過(guò)程中，我們分別選用了權(quán)數(shù)Wlt= 則=丄 W3t=-。經(jīng)估計(jì)檢XtXtJ Xt驗(yàn)發(fā)現(xiàn)用權(quán)數(shù)W的效果最好。得結(jié)果為下表9Dependent Variable: YMethod: Least SquaresDate: 12/17/13 Time: 21:37Sample: 1982 2011Included observations: 30VariableCoefficientStd. Errort-StatisticProb.C771.6726299.33712.5779380.0160X10.99

25、74110.1518216.5696650.0000X2-0.3570550.125358-2.8482810.0085X3-18.077363.072124-5.8843190.0000Weighted StatisticsWeighting series: W1R-squared0.982037 Mean dependent var4831.524Adjusted R-squared0.979964S.D. dependent var2036.795S.E. of regression706.8518Akaike info criterion16.08308Sum squared resi

26、d12990627Schwarz criterion16.26991Log likelihood-237.2463Hannan-Quinn criter.16.14285F-statistic473.7984Durbin-Watson stat0.616906Prob(F-statistic)0.000000Unweighted StatisticsR-squared0.978115Mean dependent var30292.65Adjusted R-squared0.975590S.D. dependent var37699.18S.E. of regression5890.047Sum squared resid9.02E+08Durbin-Watson stat0.382478估計(jì)結(jié)果如下：Yt= 771.6726+0.997411X1-0.357055X2-18