我国能源消耗影响计量分析

1、计量经济学课程论文倡导节约型社会，推进集约型发展对我国能源消耗影响的计量分析小组成员：（研究生 2005 级 1 班 经济学）龙楚瑜 武英杰 钱晓烨 易洁 黄瑞琦指导教师：鲁万波日期： 2005 年 9 月 12 月摘要：中国经济稳步快速增长，举世瞩目。但是中国能源发展存在的问题，如总 量不足，石油紧缺，环境污染严重，人均占有量少等，已开始阻碍中国经济的发 展。本文在分析我国的能源消耗问题上，首先通过定性分析得到了 5 个自变量， 之后，使用Eviews将所有确定的变量进行线性回归后，得到了含有5个自变量的初步的模型。 对模型进行检验的时候， 我们首先从经济意义， 统计推断进行检 验，删除了方

2、程中与经济意义相悖的变量，再在Eviews软件的辅助下进行了计量检验，修正了模型存在的多重共线性， 使得模型通过了关于异方差和自相关的 检验，最终得到了较满意的模型， 得出了我国的能源消耗量与可供消耗的能源量 和我国的人口数相关关系较强这一结论。关键词：能源消耗 线性回归 多重共线性 异方差 自相关 节约一、选题意义及背景目前我国能源消耗惊人，形势严峻。根据统计显示，从 1990 年到 2001年， 我国石油消耗增长 100%，天然气增长 92%。2002 年，全国一次能源消费量为 14.8 亿吨标准煤，居世界第二位； 2003 年上升到 16.78 亿吨标准煤，增长 10.15%， 超过GD

3、P增长9.1%的速度。其中原油消费量 2.52亿吨，增长12%原煤15.79 亿吨，增长 13.6%。国家有关部门预计，到 2010年，我国煤炭产量将有 2.5 亿 吨的缺口。到 2020年，缺口将达到 7亿吨。以目前的趋势计算，到 2020 年,我国能源消费总量将超过 36 亿吨标准煤， “十一五”期末的能源消费总量应在 25亿吨标准煤左右。“十五”的经验证明： “十五”初期有关能源需求的预测 ,低于实际的需求水平， 2005年我国消费的能 源总量，事实上已经存在透支。因此研究我国能源的消耗量具有切实意义， 这在对于减少能源的消耗， 构建 节约型社会有着一定的作用。根据经验表明， 我国的能源

4、消耗受到许多因素的影响， 这些因素包括： 能源 的供给量，能源的需求量，还包括我国的人口数量，对外贸易总额，还有原材料 的价格以及国内生产总值都会对能源的消耗产生影响。 因此在模型中， 将引入这 些解释变量对我国的能源消耗进行回归分析。二、模型的建立2.1 变量的说明及模型的建立根据以上的分析，我们引入了 5个自变量分析影响我国能源的消耗：yt ：能源消耗总量（单位：万吨标准煤） ；x1t ：可供消费的能源量（单位：万吨标准煤） ；X2t :进出口贸易总额（单位：亿元）；x3t:国内生产总值（单位：亿元） x4t :我国的年底总人口数;X5t :主要原材料、燃料、动力购进价格分类指数（上年=1

5、00）;t为年份数,t =1988-2004i :待估计参数，i =0,1,25 ；根据引入的变量，设定模型为yt = - 0 - 1Xit - 2X2t - 3X3t - 4x4 5x5t Ut 其中Ut为随机扰动项。2.2模型的估计2.2.1原始数据通过数据收集和整理，我们得到了相关变量的数据：能源消耗总量可供消耗的能源量进出口贸易总额国内生产总值我国的年底总人口数购进价格分类指数198892997932353821.8198996934953264156126.4199098703961385560.118547.9114333105.619911037831001957225.8216

6、17.8115823109.119921091701048809119.626638.111717111119931159931116201127134634.4118517135.1199412273711796720381.946759.4119850118.2199513117612953523499.958478.1121121115.3199613894813443324133.867884.6122389103.9199713779813372426967.274462.6123626101.3199813221412836826849.778345.212476195.81999

7、13011911582929896.282067.512578696.7200013029711515039273.289468.1126743105.1200113491412531042183.697314.812762799.8200214822214431951378.2105172.312845397.7200317094316848770483.5117390.2129227104.8200419700095539.1136875.9129988111.42.2.2模型的估计将收集到的数据导入Eviews软件，估计参数，得到输出结果为:表1:Depe ndent Variable:

8、 YMethod: Least SquaresDate: 12/07/05 Time: 09:26Sample (adjusted): 1990 2003In eluded observati ons: 14 after adjustme nts数据来源：根据中国统计年鉴数据计算整理所得VariableCoefficie ntStd. Errort-StatisticProb.X10.6788490.0873757.7693730.0001X2-0.0028760.144687-0.0198800.9846X30.1019750.3477710.2932240.7768X40.7561851.

9、7313760.4367540.6738X569.8813296.619210.7232650.4901C-61513.01192876.1-0.3189250.7580R-squared0.991616Mean depe ndent var128929.8Adjusted R-squared0.986376S.D. dependent var18632.92S.E. of regressi on2174.846Akaike info criteri on18.50483Sum squared resid37839634Schwarz criteri on18.77871Log likelih

10、ood-123.5338F-statistic189.2439Durb in-Watson stat2.298592Prob(F-statistic)0.000000根据该结果整理得到回归方程:% - -6151301 0.678849x1t - 0.002876x2t 0.101975x3t 0.756185x4t 69.88132x5tt -(-0.318925) (7.769373) (-0.019880) (0.293224)(0.436754)(0.723265)R2 =0.991616 R2 =0.986376 DW = 2.298592三、模型检验3.1经济意义的检验根据以上的回

11、归结果可以看到，我国能源消耗总量与可供消费的能源量， 国 内生产总值，我国的年底总人口数，主要原材料、燃料、动力购进价格分类指数 呈线性正相关关系，与实际意义相符合；但是该结果同时表明能源消耗总量与进 出口贸易总额呈线性负相关关系，这与实际意义不相符合，因此应该考虑将该变 量删去。3.2统计推断检验根据以上的回归结果得到，判定系数为R2 =0.991616，修正的判定系数为定:=0.05的条件下,R2 =0.986376，F统计量为189.2439,模型的整体拟合优度相当好；但是在给除了百之外的所有变量的P值都大于0.05,则应当接受原 假设，拒绝备择假设，认为我国能源消耗总量与进出口贸易总额

12、， 国内生产总值, 我国的年底总人口数，主要原材料、燃料、动力购进价格分类指数没有显著的相 关关系。3.3计量经济学的检验以及模型的修正3.3.1对于多重共线性1 .检验根据表1的输出结果，可以看到F统计量显著，方程整体显著，但是变量X2t , X3t , X4t , X5t的t统计量均没有通过检验，方程存在着严重的多重共线性。同时，结合相关系数矩阵，也可以看出各自变量之间相关系数很高，确实可能存在严重的多重共线性。X1X2X3X4X5X110.868400.825240.77491-0.30486X20.8684010.949580.92147-0.44293X30.825240.94958

13、10.99222-0.56054X40.774910.921470.992221-0.54035X5-0.30486-0.44293-0.56054-0.5403512.模型的修正对于多重共线性的修正在此选取逐步回归法。在对模型进行经济意义检验时，由于x2t与经济意义相悖，在修正时先将其去掉，整理得到回归结果为：yt 二-63444.66 0.679002x1t0.097443x3t 0.774370x4t68.96858x5tt =(-0.403876)(8.274438)(-0.393505)(0.558716)(0.860451)R2 =0.991616R2 =0.987889 DW =

14、2.291903可以看到模型仍然存在严重的多重共线性。为了修正多重共线性，我们首先将模型中的各个自变量分别与应变量进行回 归，结果如下：y与x1t进行拟合：Depe ndent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:13Sample (adjusted): 1988 2003In cluded observati ons: 16 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X11.0083850.05691417.717620.0000C

15、4023.6726901.1130.5830470.5691R-squared0.957306Mean depe ndent var124684.3Adjusted R-squared0.954256S.D. dependent var20880.48S.E. of regressi on4465.871Akaike info criteri on19.76278Sum squared resid2.79E+08Schwarz criteri on19.85936Log likelihood-156.1023F-statistic313.9140y与x3t进行拟合:Depe ndent Var

16、iable: YMethod: Least SquaresDate: 12/07/05 Time: 11:14Sample (adjusted): 1990 2004In eluded observati ons: 15 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X30.6433280.0755258.5181160.0000C88192.235927.73514.877900.0000R-squared0.848057Mean depe ndent var133467.8Adjusted R-squared

17、0.836369S.D. dependent var25125.49S.E. of regressi on10163.60Akaike info criteri on21.41458Sum squared resid1.34E+09Schwarz criteri on21.50899Log likelihood-158.6093F-statistic72.55829Durb in -Watson stat0.468700Prob(F-statistic)0.000001y与x4t进行拟合:Depe ndent Variable: YMethod: Least SquaresDate: 12/0

18、7/05 Time: 11:14Sample (adjusted): 1990 2004In eluded observati ons: 15 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X44.2195820.7256725.8147270.0001C-385657.589348.14-4.3163470.0008R-squared0.722288Mean depe ndent var133467.8Adjusted R-squared0.700925S.D. dependent var25125.49S.E

19、. of regressi on13740.56Akaike info criteri on22.01766Sum squared resid2.45E+09Schwarz criteri on22.11206Log likelihood-163.1324F-statistic33.81105Durb in -Watson stat0.497642Prob(F-statistic)0.000060y与x5t进行拟合:Depe ndent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:14Sample (adjusted): 19

20、89 2004In cluded observati ons: 16 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X5-746.0181603.7322-1.2356770.2369C212183.465859.273.2217690.0061R-squared0.098339Mean depe ndent var131184.4Adjusted R-squared0.033935S.D. dependent var25935.00S.E. of regressi on25491.16Akaike info c

21、riteri on23.24652Sum squared resid9.10E+09Schwarz criteri on23.34309Log likelihood-183.9722F-statistic1.526898Durb in -Watson stat0.331306Prob(F-statistic)0.236912选出其中拟合优度最好的为：yt =4023.672 1.008385x1tt -( -0.403876) (8.274438)R2 =0.957306R2 =0.954256 DW =0.734945再将其余3个变量逐个分别加入该模型中，结果如下：y与x1t，x3t进行拟合

22、Depe ndent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:21Sample (adjusted): 1990 2003In cluded observati ons: 14 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X10.6705500.05311512.624400.0000X30.2052920.0314326.5312160.0000C32789.985014.0756.5395870.0000R-squared0.98972

23、9Mean depe ndent var128929.8Adjusted R-squared0.987862S.D. dependent var18632.92S.E. of regressi on2052.875Akaike info criteri on18.27928Sum squared resid46357262Schwarz criteri on18.41622Log likelihood-124.9550F-statistic529.9885Durb in -Watson stat1.931107Prob(F-statistic)0.000000y与x1t, x4t进行拟合Dep

24、e ndent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:21Sample (adjusted): 1990 2003In cluded observati ons: 14 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X10.7130780.04464015.974130.0000X41.2292790.1744507.0465810.0000C-109604.417468.57-6.2743790.0001R-squared0.990914

25、Mean depe ndent var128929.8Adjusted R-squared0.989262S.D. dependent var18632.92S.E. of regressi on1930.830Akaike info criteri on18.15670Sum squared resid41009154Schwarz criteri on18.29364Log likelihood-124.0969F-statistic599.8230Durb in -Watson stat2.246790Prob(F-statistic)0.000000y与x1t,x5t进行拟合Depe

26、ndent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:22Sample (adjusted): 1989 2003In cluded observati ons: 15 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X10.9398280.06139715.307450.0000X5-177.2378107.2339-1.6528140.1243C31894.2116248.331.9629220.0733R-squared0.963009Me

27、an depe ndent var126796.7Adjusted R-squared0.956844S.D. dependent var19764.50S.E. of regressi on4105.868Akaike info criteri on19.65508Sum squared resid2.02E+08Schwarz criteri on19.79669Log likelihood-144.4131F-statistic156.2033Durb in -Watson stat1.067578Prob(F-statistic)0.000000其中拟合优度最好的为：yt 109604

28、.4 0.713078&1.229279x4tt = (-6.274379) (15.97413)(7.046581)R2 =0.990914 R2 =0.989262 DW = 2.246790再将剩余的2个变量逐个分别加入该模型中，从输出结果(见下)可以看到，不 管加入的变量为x3t还是x5t，均不能通过t检验。y与x1t，x3t，x4t进行拟合Depe ndent Variable: YMethod: Least SquaresDate: 12/0705 Time: 11:30Sample (adjusted): 1990 2003In cluded observati ons: 14

29、after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X10.7186240.06703410.720230.0000X3-0.0232660.201392-0.1155270.9103X41.3649331.1883771.1485700.2775C-125383.1137801.8-0.9098800.3843R-squared0.990926Mean depe ndent var128929.8Adjusted R-squared0.988204S.D. dependent var18632.92S.E. of r

30、egressi on2023.722Akaike info criteri on18.29822Sum squared resid40954495Schwarz criteri on18.48081Log likelihood-124.0875F-statistic364.0187Durb in -Watson stat2.268874Prob(F-statistic)0.000000y与 x1t，x4t，x5t进行拟合Depe ndent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:37Sample (adjusted):

31、1990 2003In cluded observati ons: 14 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.X10.7050400.04643615.183140.0000X41.3131790.2053996.3933010.0001X551.1000663.198120.8085690.4376C-124366.625463.52-4.8841080.0006R-squared0.991472Mean depe ndent var128929.8Adjusted R-squared0.988913

32、S.D. dependent var18632.92S.E. of regressi on1961.952Akaike info criteri on18.23622Sum squared resid38492570Schwarz criteri on18.41881Log likelihood-123.6536F-statistic387.5140Durb in -Watson stat2.330483Prob(F-statistic)0.000000因此在经过多重共线性的修正以后，我们最终得到的模型为：yt 二 -109604.4 0.713078x1t 1.229279x4tt 二(-6

33、.274379) (15.97413)(7.046581)R2 =0.990914R2 =0.989262 DW = 2.2467903.3.2异方差的检验1、White 检验对于异方差的检验，首先选择 White检验，得到输出结果为:White Heteroskedasticity Test:F-statistic2.230259Probability0.149849Obs*R-squared8.151832Probability0.148064Test Equati on:Depe ndent Variable: RESIDA2Method: Least SquaresDate: 12/1

34、6/05 Time: 10:16Sample (adjusted): 1990 2003In eluded observati ons: 14 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.C-1.73E+092.19E+09-0.7906190.4520X1-8308.1146576.899-1.2632270.2421X1A2-0.0098060.005714-1.7161540.1245X1*X40.0862850.0608041.4190800.1936X435696.1241501.400.860119

35、0.4148X4A2-0.1844790.194738-0.9473160.3712R-squared0.582274Mean depe ndent var2929225.Adjusted R-squared0.321195S.D. dependent var3350446.S.E. of regressi on2760420.Akaike info criteri on32.79719Sum squared resid6.10E+13Schwarz criteri on33.07107Log likelihood-223.5803F-statistic2.230259Durb in -Wat

36、son stat2.734672Prob(F-statistic)0.149849表中结果显示，P值较大，明显大于显著性水平:=0.05，可以初步判定模 型不存在异方差。2、ARCH检验对经过多重共线性修正后的模型选取采用 ARCH检验，选择滞后期数为3, 得到输出结果为：表2：ARCH Test:F-statistic0.588618Probability0.641651Obs*R-squared2.215915Probability0.528820Test Equati on:Depe ndent Variable: RESIDA2Method: Least SquaresDate: 12

37、/07/05 Time: 10:35Sample (adjusted): 1993 2003In eluded observati ons: 11 after adjustme ntsVariableCoefficie ntStd. Errort-StatisticProb.C2415514.1758278.1.3737950.2119RESIDA2(-1)-0.1111710.361472-0.3075510.7674RESIDA2(-2)0.3926290.3771841.0409480.3325RESIDA2(-3)0.2400180.4036340.5946430.5708R-squared0.201447Mean depe ndent var3653173.Adjusted R-squared-0.140790S.D.dependent var3447034.S.E. of regressi on3681701.Akaike info criteri on33.35094Sum squared resid9.49E+13Schwarz criteri on33.49562Log likelihood-179.4301F-statistic0.588618Durb in-Watson stat2.183698Prob(F-statistic)0.641651查表可得：