吴洋一 经济学 0807100242 2011年本科计量经济学实验课程期末上机考试实验报告

1、实 验（实训）报 告项 目 名 称 建立影响能源消费需求总量的因素模型所属课程名称 计量经济学实验 项 目 类 型 多重共线性模型的检验与处理 实验(实训)日期 2011年4月14日 班 级 08经济（2） 学 号 0807100242 姓 名 吴洋一 指导教师 项后军 浙江财经学院教务处制一、实验（实训）概述：【目的及要求】(1)建立对数线性多元回归模型(2)如果决定用表中全部变量作为解释变量，你预料会遇到多重共线性的问题吗？为什么？(3)如果有多重共线性，你准备怎样解决这个问题？试写出整个分析和解决过程。【基本原理】 Klein判别法，逐步回归法,OLS【实施环境】（使用的材料、设备、软件

2、）1、电脑1人一台。2、Eviews3.1学生版二、实验（实训）内容：【项目内容】建立并检验影响影响能源消费需求总量的因素模型【方案设计】理论上认为影响能源消费需求总量的因素主要有经济发展水平、收入水平、产业发展、人民生活水平提高、能源转换技术等因素。为此，收集了中国能源消费总量Y (万吨标准煤)、国内生产总值(亿元)X1(代表经济发展水平)、国民总收入(亿元)X2(代表收入水平)、工业增加值(亿元)X3、建筑业增加值(亿元)X4、交通运输邮电业增加值(亿元)X5(代表产业发展水平及产业结构)、人均生活电力消费 (千瓦小时)X6(代表人民生活水平提高)、能源加工转换效率(%)X7(代表能源转换

3、技术)等在1985-2002年期间的统计数据，具体如下：年份能源消费国民总收入工业建筑业交通运输邮电人均生活电力消费能源加工转换效率yX1X2X3X4X5X6X71985766828989.18964.43448.7417.9406.921.368.2919868085010201.410202.23967.0525.7475.623.268.3219878663211954.511962.54585.8665.8544.926.467.4819889299714922.314928.35777.2810.0661.031.266.5419899693416917.816909.26484.0

4、794.0786.035.366.5119909870318598.418547.96858.0859.41147.542.467.2199110378321662.521617.88087.11015.11409.746.965.9199210917026651.926638.110284.51415.01681.854.666199311599334560.534634.414143.82284.72123.261.267.32199412273746670.046759.419359.63012.62685.972.765.2199513117657494.958478.124718.3

5、3819.63054.783.571.05199613894866850.567884.629082.64530.53494.093.171.5199713779873142.774462.632412.14810.63797.2101.869.23199813221476967.278345.233387.95231.44121.3106.669.44199913011980579.482067.535087.25470.64460.3118.170.45200013029788254.089468.139047.35888.05408.6132.470.96200113491495727.

6、997314.842374.66375.45968.3144.670.412002148222103935.3105172.345975.27005.06420.3156.369.78资料来源：中国统计年鉴2004、2000年版，中国统计出版社。【实验（实训）过程】（步骤、记录、数据、程序等）一、建立对数线性多元回归模型利用Eviews软件，输入Y、X1、X2、X3、X4、X5、X6、X7等数据，采用这些数据对模型进行OLS回归，结果如表1.1： 表1.1Dependent Variable: YMethod: Least SquaresDate: 06/14/11 Time: 10:20Sa

7、mple: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-80155.52108510.7-0.7386880.4771X136.8423211.641463.1647500.0101X2-28.2335011.33756-2.4902620.0320X3-10.326374.845876-2.1309610.0589X4-17.5264317.94658-0.9765890.3518X5-34.4999518.88123-1.8272090.0976X6336.4866992

8、.14180.3391520.7415X71952.5731535.8321.2713450.2324R-squared0.964563 Mean dependent var114898.3Adjusted R-squared0.939758 S.D. dependent var22162.37S.E. of regression5439.605 Akaike info criterion20.34190Sum squared resid2.96E+08 Schwarz criterion20.73762Log likelihood-175.0771 F-statistic38.88476Du

9、rbin-Watson stat1.842204 Prob(F-statistic)0.000002Estimation Command:=LS Y C X1 X2 X3 X4 X5 X6 X7Estimation Equation:=Y = C(1) + C(2)*X1 + C(3)*X2 + C(4)*X3 + C(5)*X4 + C(6)*X5 + C(7)*X6 + C(8)*X7Substituted Coefficients:=Y = -80155.51982 + 36.84232026*X1 - 28.23350483*X2 - 10.32637318*X3 - 17.52642

10、8*X4 - 34.49995433*X5 + 336.4865768*X6 + 1952.572512*X7二、如果决定用表中全部变量作为解释变量，你预料会遇到多重共线性的问题吗？为什么？由表1.1可见，该模型R2=0.964563，可决系数很高，F检验值38.88476,明显显著。但是当 时 , 2.228，不仅X1、X2、X3、X4、X5、X6、X7的t检验不显著，而且X2、X3、X4、X5系数的符号与预期的相反，这表明很可能存在严重的多重共线性。计算各解释变量的相关系数，选择X1、X2、X3、X4、X5、X6、X7数据，点”view/correlations”得相关系数矩阵（如表1.2

11、）：表1.2由相关系数矩阵可以看出：各解释变量相互之间的相关系数较高，证实确实存在严重多重共线性。三、消除多重共线性采用逐步回归的办法，去检验和解决多重共线性问题。分别作Y对X1、X2、X3、X4、X5、X6、X7的一元回归， Dependent Variable: YMethod: Least SquaresDate: 06/14/11 Time: 10:46Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C85243.953524.48124.186240.000

12、0X10.6249740.06154510.154810.0000R-squared0.865682 Mean dependent var114898.3Adjusted R-squared0.857287 S.D. dependent var22162.37S.E. of regression8372.365 Akaike info criterion21.00770Sum squared resid1.12E+09 Schwarz criterion21.10663Log likelihood-187.0693 F-statistic103.1201Durbin-Watson stat0.

13、253364 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06/14/11 Time: 10:46Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C85469.493523.76724.255150.0000X20.6128460.06066810.101630.0000R-squared0.864456 Mean dependent var114898.3Adjus

14、ted R-squared0.855985 S.D. dependent var22162.37S.E. of regression8410.478 Akaike info criterion21.01678Sum squared resid1.13E+09 Schwarz criterion21.11571Log likelihood-187.1511 F-statistic102.0429Durbin-Watson stat0.254758 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06

15、/14/11 Time: 10:47Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C87111.433531.74124.665290.0000X31.3700070.1415579.6781130.0000R-squared0.854102 Mean dependent varAdjusted R-squared0.844984 S.D. dependent varS.E. of regression8725.793 Akaike info criterionSu

16、m squared resid1.22E+09 Schwarz criterionLog likelihood-187.8135 F-statisticDurbin-Watson stat0.238854 Prob(F-statistic)Dependent Variable: YMethod: Least SquaresDate: 06/14/11 Time: 10:48Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C88024.823384.30526.0097

17、20.0000X48.8059490.8901559.8925990.0000R-squared0.859481 Mean dependent varAdjusted R-squared0.850698 S.D. dependent varS.E. of regression8563.442 Akaike info criterionSum squared resid1.17E+09 Schwarz criterionLog likelihood-187.4755 F-statisticDurbin-Watson stat0.244443 Prob(F-statistic)Dependent

18、Variable: YMethod: Least SquaresDate: 06/14/11 Time: 10:48Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C87474.563855.29522.689460.0000X510.147081.1608658.7409630.0000R-squared0.826848 Mean dependent varAdjusted R-squared0.816026 S.D. dependent varS.E. of re

19、gression9505.923 Akaike info criterionSum squared resid1.45E+09 Schwarz criterionLog likelihood-189.3549 F-statisticDurbin-Watson stat0.291497 Prob(F-statistic)Dependent Variable: YMethod: Least SquaresDate: 06/14/11 Time: 10:49Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort

20、-StatisticProb. C79984.114307.68618.567770.0000X6464.971149.907419.3166750.0000R-squared0.844359 Mean dependent var114898.3Adjusted R-squared0.834631 S.D. dependent var22162.37S.E. of regression9012.457 Akaike info criterion21.15504Sum squared resid1.30E+09 Schwarz criterion21.25397Log likelihood-18

21、8.3954 F-statistic86.80043Durbin-Watson stat0.270852 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06/14/11 Time: 10:49Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-342804.1151437.7-2.2636640.0378X76689.4912212.4283.0235970.0081R

22、-squared0.363618 Mean dependent var114898.3Adjusted R-squared0.323844 S.D. dependent var22162.37S.E. of regression18223.83 Akaike info criterion22.56329Sum squared resid5.31E+09 Schwarz criterion22.66222Log likelihood-201.0696 F-statistic9.142136Durbin-Watson stat0.500653 Prob(F-statistic)0.008072结果

23、如表1.3所示：表1.3变量X1X2X3X4X5X6X7参数估计值0.6249740.6128461.3700078.80594910.14708464.97116689.491t统计量10.1548110.101639.6781139.8925998.7409639.3166753.0235970.8656820.8644560.8541020.8594810.8268480.8443590.363618按的大小排序为：X1、X2、X4、X3、X6、X5、X7。以X1为基础，顺次加入其他变量逐步回归。首先加入X2回归结果为：Dependent Variable: YMethod: Least

24、 SquaresDate: 06/14/11 Time: 11:06Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C82091.424623.98117.753410.0000X110.281419.2079351.1165820.2817X2-9.4759749.035652-1.0487320.3109R-squared0.874858 Mean dependent varAdjusted R-squared0.858172 S.D. dependent var

25、S.E. of regression8346.365 Akaike info criterionSum squared resid1.04E+09 Schwarz criterionLog likelihood-186.4325 F-statisticDurbin-Watson stat0.309126 Prob(F-statistic)Y = 82091.42296 + 10.28141191*X1 - 9.475973692*X2 t=(1.116582) (-1.048732) R2=0.874858当取时，X2参数的t检验不显著，故剔除X2，再加入X3回归得Dependent Vari

26、able: YMethod: Least SquaresDate: 01/03/10 Time: 13:44Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C69047.095858.23911.786320.0000X16.6844341.9211973.4793060.0034X3-13.377104.239913-3.1550400.0065R-squared0.919261 Mean dependent var114898.3Adjusted R-square

27、d0.908496 S.D. dependent var22162.37S.E. of regression6704.025 Akaike info criterion20.60982Sum squared resid6.74E+08 Schwarz criterion20.75821Log likelihood-182.4883 F-statistic85.39239Durbin-Watson stat0.826375 Prob(F-statistic)0.000000Y = 69047.08508 + 6.684434324*X1 - 13.37709588*X3 t=(3.479306)

28、 (-3.155040) R2=0.919261当取 时，X3参数通过t检验，再加入X4回归得Dependent Variable: YMethod: Least SquaresDate: 01/03/10 Time: 13:48Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C70482.846619.40710.647910.0000X16.4934082.0044073.2395660.0059X3-14.253904.667455-3.0538920.0086

29、X48.32701516.128320.5162980.6137R-squared0.920770 Mean dependent var114898.3Adjusted R-squared0.903792 S.D. dependent var22162.37S.E. of regression6874.191 Akaike info criterion20.70207Sum squared resid6.62E+08 Schwarz criterion20.89993Log likelihood-182.3186 F-statistic54.23356Durbin-Watson stat0.9

30、20113 Prob(F-statistic)0.000000Y = 70482.84388 + 6.49340836*X1 - 14.25390404*X3 + 8.327015085*X4t=(3.239566) (-3.053892) （0.516298） R2=0.920770当取 时， ，X4参数的t检验不显著，故剔除X4，再加入X5回归得Dependent Variable: YMethod: Least SquaresDate: 01/03/10 Time: 13:53Sample: 1985 2002Included observations: 18VariableCoeffi

31、cientStd. Errort-StatisticProb. C65480.885394.77012.137850.0000X18.1638301.8125814.5039800.0005X3-14.900183.797770-3.9234030.0015X5-13.223395.753094-2.2984830.0375R-squared0.941382 Mean dependent var114898.3Adjusted R-squared0.928821 S.D. dependent var22162.37S.E. of regression5912.807 Akaike info c

32、riterion20.40076Sum squared resid4.89E+08 Schwarz criterion20.59862Log likelihood-179.6068 F-statistic74.94427Durbin-Watson stat1.171072 Prob(F-statistic)0.000000Y = 65480.88431 + 8.163829785*X1 - 14.90018308*X3 - 13.22338926*X5t=(4.503980) (-3.923403) （-2.298483） R2=0.941382当取 时， ，X5参数通过t检验，再加入X6回归

33、得Dependent Variable: YMethod: Least SquaresDate: 01/03/10 Time: 13:57Sample: 1985 2002Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C64354.2212094.415.3209910.0001X18.0303622.2690213.5391310.0036X3-14.662234.543937-3.2267670.0066X5-14.9044117.07428-0.8729160.3985X695.5724290

34、9.51620.1050810.9179R-squared0.941431 Mean dependent var114898.3Adjusted R-squared0.923410 S.D. dependent var22162.37S.E. of regression6133.405 Akaike info criterion20.51102Sum squared resid4.89E+08 Schwarz criterion20.75835Log likelihood-179.5992 F-statistic52.24043Durbin-Watson stat1.150509 Prob(F-statistic)0.000000Y = 64354.22244 + 8.030361821*X1 - 14.66222679*X3 - 14.90440504*X5 + 95.57242353*X6t=(3.539131) (-3.226767) （-0.872916） （0.105081） R2=