我国能源消耗影响计量分析

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GDP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=100）t为年份数,t=1988-2004'■i待估计参数，i'■i待估计参数，i=0,1,2…5；根据引入的变量，设定模型为yt=-根据引入的变量，设定模型为yt=-0■- ixit ■-2x2t ■-3x3t■- 4x4A■ 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①通过数据收集和整理，我们得到了相关变量的数据量源量易总额198892997量源量易总额198892997932353821.8198996934953264156199098703961385560.118547.919911037831001957225.821617.819921091701048809119.626638.119931159931116201127134634.4199412273711796720381.946759.4199513117612953523499.958478.1199613894813443324133.867884.6199713779813372426967.274462.6199813221412836826849.778345.2199913011911582929896.282067.5200013029711515039273.289468.1200113491412531042183.697314.8200214822214431951378.2105172.3200317094316848770483.5117390.2200419700095539.1136875.9能源消耗总 可供消耗的能 进出口贸国内生产总值126.4114333105.6115823109.1117171111118517135.1119850118.2121121115.3122389103.9123626101.312476195.812578696.7126743105.112762799.812845397.7129227104.8129988111.4我国的年底 购进价格分类总人口数 指数2.2.2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:DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:09:26Sample(adjusted):19902003Ineludedobservations:14afteradjustments数据来源：根据《中国统计年鉴》数据计算整理所得VariableCoefficientStd.Errort-StatisticProb.X10.6788490.0873757.7693730.0001X2-0.0028760.144687-0.0198800.9846X30.1019750.3477710.2932240.7768X40.7561851.7313760.4367540.6738X569.8813296.619210.7232650.4901C-61513.01192876.1-0.3189250.7580R-squared0.991616Meandependentvar128929.8AdjustedR-squared0.986376S.D.dependentvar18632.92S.E.ofregression2174.846Akaikeinfocriterion18.50483Sumsquaredresid37839634Schwarzcriterion18.77871Loglikelihood-123.5338F-statistic189.2439Durbin-Watsonstat2.298592Prob(F-statistic)0.000000根据该结果整理得到回归方程:%--61513010.678849x1t-0.002876x2t0.101975x3t0.756185x4t69.88132x5tt-(-0.318925)(7.769373)(-0.019880)(0.293224) (0.436754)(0.723265)R2=0.991616R2=0.986376DW=2.298592三、模型检验3.1经济意义的检验根据以上的回归结果可以看到，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；但是在给定：=0.05的条件下，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，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。X1X2X3X4X5X110.868400.825240.77491-0.30486X20.8684010.949580.92147-0.44293X30.825240.9495810.99222-0.56054X40.774910.921470.992221-0.54035X5-0.30486-0.44293-0.56054-0.5403512.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二-63444.660.679002x1t 0.097443x3t0.774370x4t 68.96858x5tt=(-0.403876)(8.274438) (-0.393505)(0.558716) (0.860451)R2=0.991616 R2=0.987889DW=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：DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:13Sample(adjusted):19882003Includedobservations:16afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X11.0083850.056914 17.717620.0000C4023.6726901.113 0.5830470.5691R-squared0.957306Meandependentvar124684.3AdjustedR-squared0.954256S.D.dependentvar20880.48S.E.ofregression4465.871Akaikeinfocriterion19.76278Sumsquaredresid2.79E+08Schwarzcriterion19.85936Loglikelihood-156.1023F-statistic313.9140

y与x3t进行拟合:DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:14Sample(adjusted):19902004Ineludedobservations:15afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X30.6433280.075525 8.5181160.0000C88192.235927.735 14.877900.0000R-squared0.848057Meandependentvar133467.8AdjustedR-squared0.836369S.D.dependentvar25125.49S.E.ofregression10163.60Akaikeinfocriterion21.41458Sumsquaredresid1.34E+09Schwarzcriterion21.50899Loglikelihood-158.6093F-statistic72.55829Durbin-Watsonstat0.468700Prob(F-statistic)0.000001y与x4t进行拟合:DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:14Sample(adjusted):19902004Ineludedobservations:15afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X44.2195820.725672 5.8147270.0001C-385657.589348.14 -4.3163470.0008R-squared0.722288Meandependentvar133467.8AdjustedR-squared0.700925S.D.dependentvar25125.49S.E.ofregression13740.56Akaikeinfocriterion22.01766Sumsquaredresid2.45E+09Schwarzcriterion22.11206Loglikelihood-163.1324F-statistic33.81105Durbin-Watsonstat0.497642Prob(F-statistic)0.000060y与x5t进行拟合:

DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:14Sample(adjusted):19892004Includedobservations:16afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X5-746.0181603.7322 -1.2356770.2369C212183.465859.27 3.2217690.0061R-squared0.098339Meandependentvar131184.4AdjustedR-squared0.033935S.D.dependentvar25935.00S.E.ofregression25491.16Akaikeinfocriterion23.24652Sumsquaredresid9.10E+09Schwarzcriterion23.34309Loglikelihood-183.9722F-statistic1.526898Durbin-Watsonstat0.331306Prob(F-statistic)0.236912选出其中拟合优度最好的为：yt=4023.6721.008385x1tt-(-0.403876)(8.274438)R2=0.957306 R2=0.954256DW=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xlt,x3t进行拟合DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:21Sample(adjusted):19902003Includedobservations:14afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X10.6705500.053115 12.624400.0000X30.2052920.031432 6.5312160.0000C32789.985014.075 6.5395870.0000R-squared0.989729Meandependentvar128929.8AdjustedR-squared0.987862S.D.dependentvar18632.92S.E.ofregression2052.875Akaikeinfocriterion18.27928Sumsquaredresid46357262Schwarzcriterion18.41622Loglikelihood-124.9550F-statistic529.9885Durbin-Watsonstat1.931107Prob(F-statistic)0.000000

y与x1t,x4t进行拟合DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:21Sample(adjusted):19902003Includedobservations:14afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X10.7130780.044640 15.974130.0000X41.2292790.174450 7.0465810.0000C-109604.417468.57 -6.2743790.0001R-squared0.990914Meandependentvar128929.8AdjustedR-squared0.989262S.D.dependentvar18632.92S.E.ofregression1930.830Akaikeinfocriterion18.15670Sumsquaredresid41009154Schwarzcriterion18.29364Loglikelihood-124.0969F-statistic599.8230Durbin-Watsonstat2.246790Prob(F-statistic)0.000000y与x1t, x5t进行拟合DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:22Sample(adjusted):19892003Includedobservations:15afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X10.9398280.061397 15.307450.0000X5-177.2378107.2339 -1.6528140.1243C31894.2116248.33 1.9629220.0733R-squared0.963009Meandependentvar126796.7AdjustedR-squared0.956844S.D.dependentvar19764.50S.E.ofregression4105.868Akaikeinfocriterion19.65508Sumsquaredresid2.02E+08Schwarzcriterion19.79669Loglikelihood-144.4131F-statistic156.2033Durbin-Watsonstat1.067578Prob(F-statistic)0.000000其中拟合优度最好的为：yt—109604.40.713078& 1.229279x4tt=(-6.274379)(15.97413) (7.046581)R2=0.990914R2=0.989262DW=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DependentVariable:YMethod:LeastSquaresDate:12/0705Time:11:30Sample(adjusted):19902003Includedobservations:14afteradjustmentsVariableCoefficientStd.Error t-StatisticProb.X10.7186240.067034 10.720230.0000X3-0.0232660.201392 -0.1155270.9103X41.3649331.188377 1.1485700.2775C-125383.1137801.8 -0.9098800.3843R-squared0.990926Meandependentvar128929.8AdjustedR-squared0.988204S.D.dependentvar18632.92S.E.ofregression2023.722Akaikeinfocriterion18.29822Sumsquaredresid40954495Schwarzcriterion18.48081Loglikelihood-124.0875F-statistic364.0187Durbin-Watsonstat2.268874Prob(F-statistic)0.000000y与x1t,x4t, x5t进行拟合DependentVariable:YMethod:LeastSquaresDate:12/07/05Time:11:37Sample(adjusted):19902003Includedobservations:14afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.X10.7050400.04643615.183140.0000X41.3131790.2053996.3933010.0001X551.1000663.198120.8085690.4376C-124366.625463.52-4.8841080.0006

R-squared0.991472Meandependentvar128929.8AdjustedR-squared0.988913S.D.dependentvar18632.92S.E.ofregression1961.952Akaikeinfocriterion18.23622Sumsquaredresid38492570Schwarzcriterion18.41881Loglikelihood-123.6536F-statistic387.5140Durbin-Watsonstat2.330483Prob(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二-109604.40.713078x1t1.229279x4tt二(-6.274379)(15.97413) (7.046581)R2=0.990914 R2=0.989262DW=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:WhiteHeteroskedasticityTest:F-statistic 2.230259 Probability 0.149849Obs*R-squared 8-151832 Probability 0-148064TestEquation:DependentVariable:RESIDA2Method:LeastSquaresDate:12/16/05Time:10:16Sample(adjusted):19902003Ineludedobservations:14afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C-1.73E+092.19E+09-0.7906190.4520X1-8308.1146576.899-1.2632270.2421X1A2-0.0098060.005714-1.7161540.1245X1*X40.0862850.0608041.4190800.1936X435696.1241501.400.8601190.4148X4A2-0.1844790.194738-0.9473160.3712R-squared0.582274Meandependentvar2929225.AdjustedR-squared0.321195S.D.dependentvar3350446.S.E.ofregression2760420.Akaikeinfocriterion32.79719Sumsquaredresid6.10E+13Schwarzcriterion33.07107Loglikelihood-223.5803F-statistic2.230259Durbin-Watsonstat2.734672Prob(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：ARCHTest:F-statistic 0.588618Probability 0.641651Obs*R-squared 2.215915Probability 0.528820TestEquation:DependentVariable:RESIDA2Method:LeastSquaresDate:12/07/05Time:10:35Sample(adjusted):19932003Ineludedobservations:11afteradjustmentsVariable CoefficientStd.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-squared0.201447Meandependentvar3653173.AdjustedR-squared-0.140790S.D.dependentvar3447034.S.E.ofregression3681701.AkaikeinfocriterionSchwarzcriterion33.35094Sumsquaredresid9.49E+1333.49562Loglikelihood-179.4301F-statistic0.588618Durbin-Watsonstat2.183698Prob(F-statistic)0.641651查表可得： ’爲(3)=7.81，而obs*R2=2.2