試探交通運(yùn)輸發(fā)展與國(guó)民經(jīng)濟(jì)的關(guān)系

1、試探交通運(yùn)輸發(fā)展與國(guó)民經(jīng)濟(jì)的關(guān)系小組成員: 金融學(xué)院2001級(jí)袁新熠 李泓良 王陽(yáng) 童運(yùn)超 羅衛(wèi)平 陳東 指導(dǎo)教師 周游日期 2004年5月摘要：本文主要通過(guò)對(duì)我國(guó)1991年到2002年交通運(yùn)輸業(yè)的發(fā)展?fàn)顩r與國(guó)民經(jīng)濟(jì)的發(fā)展之間的關(guān)系進(jìn)行多因素實(shí)證分析。建立以國(guó)民經(jīng)濟(jì)指標(biāo)為應(yīng)變量，交通運(yùn)輸業(yè)的經(jīng)濟(jì)指標(biāo)為自變量的多元線性回歸模型，試圖探索交通運(yùn)輸發(fā)展與國(guó)民經(jīng)濟(jì)的關(guān)系。首先，我們收集了相關(guān)的數(shù)據(jù)，利用EVIEWS軟件對(duì)計(jì)量模型進(jìn)行了參數(shù)估計(jì)，并建立了理論模型。然后，進(jìn)行檢驗(yàn)并對(duì)模型加以修正。最后，我們結(jié)合相關(guān)的理論對(duì)所得的分析結(jié)果作了經(jīng)濟(jì)意義的分析。一.問(wèn)題的提出與猜測(cè)： “要想富，先修路”是我們大

2、家都耳熟能詳?shù)囊痪湓?，改革開(kāi)放以來(lái)，我國(guó)的交通運(yùn)輸業(yè)有了很大的發(fā)展，表現(xiàn)在運(yùn)輸線路長(zhǎng)度上和客貨運(yùn)送量上都大幅度增長(zhǎng)，與此同時(shí)，我國(guó)的經(jīng)濟(jì)發(fā)展也快速發(fā)展。二者的同步發(fā)展是否存在著某種聯(lián)系？在此，我們猜測(cè)兩者之間存在著一定的聯(lián)系，根據(jù)“要想富，先修路”這一經(jīng)驗(yàn)，我們猜測(cè)交通運(yùn)輸業(yè)對(duì)國(guó)民經(jīng)濟(jì)的發(fā)展具有先行作用，也即交通運(yùn)輸業(yè)對(duì)國(guó)民經(jīng)濟(jì)的發(fā)展具有促進(jìn)作用。以下，我們將根據(jù)這一設(shè)想，收集相關(guān)數(shù)據(jù)，并估計(jì)和檢驗(yàn)，希望能夠找出二者之間是否存在關(guān)系，如若有，它們是什么樣的關(guān)系？以及它們?cè)诙啻蟪潭壬舷嚓P(guān)？二.數(shù)據(jù)的搜集： 在進(jìn)行實(shí)證分析的過(guò)程中，所需要的數(shù)據(jù)，應(yīng)是能夠很好代表兩者水平的指標(biāo)。就國(guó)民經(jīng)濟(jì)而言，GD

3、P應(yīng)該是最合適的指標(biāo)，因?yàn)槲覀兲骄康氖墙?jīng)濟(jì)總量的問(wèn)題，我們選取了各年我國(guó)的GDP總量（雖然人均GDP也同樣有用，但明顯不及總量GDP）；對(duì)于我國(guó)交通運(yùn)輸發(fā)展?fàn)顩r的水平指標(biāo)，可選擇的余地較大，但我們發(fā)現(xiàn)它們之間存在著明顯的相關(guān)性，為了盡量避免多重共線形和使模型更加簡(jiǎn)潔精確，我們選取了四個(gè)最具有代表性的指標(biāo)，它們分別是全國(guó)全年客運(yùn)總量（用X1表示），全國(guó)全年貨運(yùn)總量（X2），截止當(dāng)年全國(guó)鐵路總里程數(shù)（X3），截止當(dāng)年全國(guó)公路總里程數(shù)（X4）。 本文中數(shù)據(jù)的起止時(shí)間是1991年到2002年，一共12年的數(shù)據(jù)。數(shù)據(jù)來(lái)源于中經(jīng)專網(wǎng)和國(guó)家統(tǒng)計(jì)局網(wǎng)站。三.對(duì)模型的猜測(cè)： 我們假設(shè)以上四個(gè)變量和GDP之間存在

4、以下的關(guān)系，待估計(jì)方程為： Y=m+aX1+bX2+cX3+dX4+u YGDP X1全國(guó)全年客運(yùn)總量 X2 全國(guó)全年貨運(yùn)總量X3 全國(guó)鐵路總里程數(shù) X4 全國(guó)公路總里程 接下來(lái)我將利用樣本數(shù)據(jù)對(duì)參數(shù)進(jìn)行估計(jì)。四.數(shù)據(jù):（單位：1萬(wàn)人，2萬(wàn)噸，3公里，4公里）年份指標(biāo)X1X2X3X4Y19915780021662.519925810026651.919935860034560.51994590004667019955970057494.919966490066850.519976600073142.719986640076967.219996740080579.420006870088254.

5、920017010095727.9200272000.6五.模型的參數(shù)估計(jì)：利用EVIEWS軟件，用OLS方法估計(jì)得：Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:30Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X10.0.11.364680.0000X20.0.1.0.1895X4-0.0.-0.0.7670X31.0.5.0.0012C-.79976.147-12.51062

6、0.0000R-squared0. Mean dependent var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E. of regression973.9812 Akaike info criterion16.89500Sum squared resid. Schwarz criterion17.09704Log likelihood-96.36999 F-statistic2130.105Durbin-Watson stat2. Prob(F-statistic)0.可得模型：Y=0.*X1+0.*X2+1.*X3-0

7、.3*X4- .7471 在上面的ols的結(jié)果中我們可以看出，變量x2與x4的p值未獲得通過(guò)，我們?cè)诮酉聛?lái)的過(guò)程中進(jìn)行檢驗(yàn)和修正。六.計(jì)量經(jīng)濟(jì)學(xué)檢驗(yàn)及其修正 1.多重共線性檢驗(yàn) 用EVIEWS軟件，得相關(guān)系數(shù)矩陣表：X1X2X4X3X110.90.50.1X20.910.10.4X40.50.110.4X30.10.40.41由上可以看出，整體上線形回歸擬合較好，但x2，x4變量的參數(shù)的t檢驗(yàn)的p值大于0.05，所以t值并不顯著，而且x4的系數(shù)符號(hào)與經(jīng)濟(jì)意義不符。兩種方法結(jié)合一起來(lái)看，解釋變量確實(shí)存在多重共線性。 下面我們利用逐步回歸法（變量剔除法）進(jìn)行修正： (1)運(yùn)用ols方法逐一求y對(duì)各

8、個(gè)解釋變量的回歸.結(jié)合經(jīng)濟(jì)意義和統(tǒng)計(jì)檢驗(yàn)選出擬合效果最好的一元線形回歸方程. (a)對(duì)x1與y回歸:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:35Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X10.0.43.934120.0000C-74544.213215.745-23.181010.0000R-squared0. Mean dependent var64343.00Adj

9、usted R-squared0. S.D. dependent var27118.34S.E. of regression2041.904 Akaike info criterion18.23217Sum squared resid Schwarz criterion18.31298Log likelihood-107.3930 F-statistic1930.207Durbin-Watson stat1. Prob(F-statistic)0.(b)對(duì)x2與y回歸:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time:

10、14:35Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X20.0.15.187110.0000C-.013973.68-10.473190.0000R-squared0. Mean dependent var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E. of regression5797.865 Akaike info criterion20.31938Sum squared resid3.

11、36E+08 Schwarz criterion20.40020Log likelihood-119.9163 F-statistic230.6482Durbin-Watson stat0. Prob(F-statistic)0.(c)對(duì)x3與y回歸:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:36Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X35.0.12.548650.00

12、00C-.126162.96-10.052380.0000R-squared0. Mean dependent var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E. of regression6950.126 Akaike info criterion20.68192Sum squared resid4.83E+08 Schwarz criterion20.76274Log likelihood-122.0915 F-statistic157.4687Durbin-Watson stat1. Prob(F-statisti

13、c)0.(d)對(duì)x4與y回歸:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:36Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X40.0.6.0.0001C-68969.2521080.72-3.0.0084R-squared0. Mean dependent var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E

14、. of regression12568.17 Akaike info criterion21.86673Sum squared resid1.58E+09 Schwarz criterion21.94755Log likelihood-129.2004 F-statistic41.21238Durbin-Watson stat0. Prob(F-statistic)0.由以上可以得知擬合程度最好的方程是: Y = 0.*X1 - 74544.21148 (43.93412) (-23.18101) R-squared=0. S.E=2041.909 F=1930.207(2)逐步回歸,將其余

15、的解釋變量逐一代入上式中,得如下幾個(gè)模型:(a) 將x2代入:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:42Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X10.0.8.0.0000X20.0.0.0.4459C-81478.419295.350-8.0.0000R-squared0. Mean dependent var64343.00Adjusted R-squared0.

16、 S.D. dependent var27118.34S.E. of regression2080.176 Akaike info criterion18.33061Sum squared resid Schwarz criterion18.45184Log likelihood-106.9837 F-statistic930.2357Durbin-Watson stat1. Prob(F-statistic)0.(b)將x3代入:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:43Sample: 1991 2

17、002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X10.0.22.059660.0000X31.0.5.0.0003C-.07567.214-15.539010.0000R-squared0. Mean dependent var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E. of regression987.2214 Akaike info criterion16.83998Sum squared resid. Schwa

18、rz criterion16.96121Log likelihood-98.03990 F-statistic4145.611Durbin-Watson stat2. Prob(F-statistic)0.(c)將x4代入:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:45Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X10.0.20.655740.0000X40.0.1.0.14

19、83C-76371.763213.872-23.763160.0000R-squared0. Mean dependent var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E. of regression1904.137 Akaike info criterion18.15376Sum squared resid Schwarz criterion18.27499Log likelihood-105.9226 F-statistic1111.059Durbin-Watson stat2. Prob(F-statistic)

20、0.由以上可以得知擬合程度最好的方程是:Y = 0.*X1 + 1.*X3 - .0399 (22.05966) (5.) (-15.53901) R-squared=0. S.E=987.2214 F=4145.611(3)再將x2,x4代入上式:(a)將x2代入:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:53Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X10.0.12.7

21、05150.0000X31.0.6.0.0003X20.0.1.0.1580C-.87881.530-15.623200.0000R-squared0. Mean dependent var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E. of regression917.2336 Akaike info criterion16.74180Sum squared resid. Schwarz criterion16.90344Log likelihood-96.45082 F-statistic3202.405Durbin-

22、Watson stat2. Prob(F-statistic)0.(b)將x4代入:Dependent Variable: YMethod: Least SquaresDate: 05/07/04 Time: 14:54Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. X10.0.20.693000.0000X31.0.4.0.0015X40.0.0.0.7394C-.08675.275-13.418020.0000R-squared0. Mean dependent

23、var64343.00Adjusted R-squared0. S.D. dependent var27118.34S.E. of regression1039.430 Akaike info criterion16.99193Sum squared resid. Schwarz criterion17.15357Log likelihood-97.95160 F-statistic2493.118Durbin-Watson stat2. Prob(F-statistic)0.由以上可知x2,x4對(duì)y的影響并不顯著,故將其刪去,得如下模型:Y = 0.*X1 + 1.*X3 - .0399 (

24、22.05966) (5.) (-15.53901) R-squared=0. S.E=987.2214 F=4145.6112.異方差檢驗(yàn)(1) ARCH檢驗(yàn)我們首先對(duì)模型進(jìn)行ARCH檢驗(yàn),得結(jié)果如下:首先對(duì)模型滯后三期:ARCH Test:F-statistic0. Probability0.Obs*R-squared1. Probability0.Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 06/09/04 Time: 08:57Sample(adjusted): 1994 2002Included

25、 observations: 9 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C2.23E+081.53E+081.0.2034RESID2(-1)0.0.0.0.5492RESID2(-2)-0.0.-0.0.4682RESID2(-3)-0.0.-0.0.9909R-squared0. Mean dependent var2.04E+08Adjusted R-squared-0. S.D. dependent var2.72E+08S.E. of regression3.11E+08 Akai

26、ke info criterion42.24730Sum squared resid4.82E+17 Schwarz criterion42.33495Log likelihood-186.1128 F-statistic0.Durbin-Watson stat0. Prob(F-statistic)0.再對(duì)模型滯后一期:ARCH Test:F-statistic1. Probability0.Obs*R-squared1. Probability0.Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 06/09

27、/04 Time: 09:01Sample(adjusted): 1992 2002Included observations: 11 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C2.93E+081.88E+081.0.1534RESID2(-1)0.0.1.0.3148R-squared0. Mean dependent var4.08E+08Adjusted R-squared0. S.D. dependent var5.16E+08S.E. of regression5.12E+08 Ak

28、aike info criterion43.10906Sum squared resid2.36E+18 Schwarz criterion43.18140Log likelihood-235.0998 F-statistic1.Durbin-Watson stat1. Prob(F-statistic)0.由以上可知,由于F和obs*R-squared的p值都大于0.05,所以其不顯著,不能拒絕原假設(shè),所以模型不存在異方差.(2)white檢驗(yàn)：我們運(yùn)用white檢驗(yàn)對(duì)模型進(jìn)行異方差的檢驗(yàn),得到如下結(jié)果:White Heteroskedasticity Test:F-statistic1.

29、Probability0.Obs*R-squared6. Probability0.Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 05/07/04 Time: 15:01Sample: 1991 2002Included observations: 12VariableCoefficientStd. Errort-StatisticProb. C-4.18E+08-0.0.8766X1-67.63222320.5373-0.0.8399X121.03E-055.57E-050.0.8600X1*X30.0.0.0.9187X33719.79919588.760.0.8557X32-0.0.-0.0.8665R-squared0. Mean dependent var.6Adjusted R-squared0. S.D. dependent var.S.E. of r