《计量经济学》课程论文.doc

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：能源消耗总量（单位：万吨标准煤）；：可供消费的能源量（单位：万吨标准煤）；：进出口贸易总额（单位：亿元）；：国内生产总值（单位：亿元）；：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(上年=100)；：待估计参数，；根据引入的变量，设定模型为其中为随机扰动项。2.2 模型的估计2.2.1 原始数据 数据来源：根据中国统计年鉴数据计算整理所得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198892997932353821.8198996934953264156126.4199098703961385560.118547.9114333105.619911037831001957225.821617.8115823109.119921091701048809119.626638.111717111119931159931116201127134634.4118517135.1199412273711796720381.946759.4119850118.2199513117612953523499.958478.1121121115.3199613894813443324133.867884.6122389103.9199713779813372426967.274462.6123626101.3199813221412836826849.778345.212476195.8199913011911582929896.282067.512578696.7200013029711515039273.289468.1126743105.1200113491412531042183.697314.812762799.8200214822214431951378.2105172.312845397.7200317094316848770483.5117390.2129227104.8200419700095539.1136875.9129988111.4 2.2.2 模型的估计将收集到的数据导入软件，估计参数，得到输出结果为：表1：Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 09:26Sample (adjusted): 1990 2003Included observations: 14 after adjustmentsVariableCoefficientStd. 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-squared0.991616Mean dependent var128929.8Adjusted R-squared0.986376S.D. dependent var18632.92S.E. of regression2174.846Akaike info criterion18.50483Sum squared resid37839634Schwarz criterion18.77871Log likelihood-123.5338F-statistic189.2439Durbin-Watson stat2.298592Prob(F-statistic)0.000000根据该结果整理得到回归方程：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，修正的判定系数为，统计量为189.2439，模型的整体拟合优度相当好；但是在给定的条件下，除了之外的所有变量的值都大于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，可以看到统计量显著，方程整体显著，但是变量的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：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：与进行拟合：Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:13Sample (adjusted): 1988 2003Included observations: 16 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X11.0083850.05691417.717620.0000C4023.6726901.1130.5830470.5691R-squared0.957306Mean dependent var124684.3Adjusted R-squared0.954256S.D. dependent var20880.48S.E. of regression4465.871Akaike info criterion19.76278Sum squared resid2.79E+08Schwarz criterion19.85936Log likelihood-156.1023F-statistic313.9140Durbin-Watson stat0.734945Prob(F-statistic)0.000000与进行拟合：Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:14Sample (adjusted): 1990 2004Included observations: 15 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X30.6433280.0755258.5181160.0000C88192.235927.73514.877900.0000R-squared0.848057Mean dependent var133467.8Adjusted R-squared0.836369S.D. dependent var25125.49S.E. of regression10163.60Akaike info criterion21.41458Sum squared resid1.34E+09Schwarz criterion21.50899Log likelihood-158.6093F-statistic72.55829Durbin-Watson stat0.468700Prob(F-statistic)0.000001与进行拟合：Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:14Sample (adjusted): 1990 2004Included observations: 15 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X44.2195820.7256725.8147270.0001C-385657.589348.14-4.3163470.0008R-squared0.722288Mean dependent var133467.8Adjusted R-squared0.700925S.D. dependent var25125.49S.E. of regression13740.56Akaike info criterion22.01766Sum squared resid2.45E+09Schwarz criterion22.11206Log likelihood-163.1324F-statistic33.81105Durbin-Watson stat0.497642Prob(F-statistic)0.000060与进行拟合：Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:14Sample (adjusted): 1989 2004Included observations: 16 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X5-746.0181603.7322-1.2356770.2369C212183.465859.273.2217690.0061R-squared0.098339Mean dependent var131184.4Adjusted R-squared0.033935S.D. dependent var25935.00S.E. of regression25491.16Akaike info criterion23.24652Sum squared resid9.10E+09Schwarz criterion23.34309Log likelihood-183.9722F-statistic1.526898Durbin-Watson stat0.331306Prob(F-statistic)0.236912选出其中拟合优度最好的为：再将其余3个变量逐个分别加入该模型中，结果如下：与，进行拟合Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:21Sample (adjusted): 1990 2003Included observations: 14 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X10.6705500.05311512.624400.0000X30.2052920.0314326.5312160.0000C32789.985014.0756.5395870.0000R-squared0.989729Mean dependent var128929.8Adjusted R-squared0.987862S.D. dependent var18632.92S.E. of regression2052.875Akaike info criterion18.27928Sum squared resid46357262Schwarz criterion18.41622Log likelihood-124.9550F-statistic529.9885Durbin-Watson stat1.931107Prob(F-statistic)0.000000与，进行拟合Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:21Sample (adjusted): 1990 2003Included observations: 14 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X10.7130780.04464015.974130.0000X41.2292790.1744507.0465810.0000C-109604.417468.57-6.2743790.0001R-squared0.990914Mean dependent var128929.8Adjusted R-squared0.989262S.D. dependent var18632.92S.E. of regression1930.830Akaike info criterion18.15670Sum squared resid41009154Schwarz criterion18.29364Log likelihood-124.0969F-statistic599.8230Durbin-Watson stat2.246790Prob(F-statistic)0.000000与，进行拟合Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:22Sample (adjusted): 1989 2003Included observations: 15 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X10.9398280.06139715.307450.0000X5-177.2378107.2339-1.6528140.1243C31894.2116248.331.9629220.0733R-squared0.963009Mean dependent var126796.7Adjusted R-squared0.956844S.D. dependent var19764.50S.E. of regression4105.868Akaike info criterion19.65508Sum squared resid2.02E+08Schwarz criterion19.79669Log likelihood-144.4131F-statistic156.2033Durbin-Watson stat1.067578Prob(F-statistic)0.000000其中拟合优度最好的为：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，不管加入的变量为还是，均不能通过检验。与，进行拟合Dependent Variable: YMethod: Least SquaresDate: 12/0705 Time: 11:30Sample (adjusted): 1990 2003Included observations: 14 after adjustmentsVariableCoefficientStd. 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 dependent var128929.8Adjusted R-squared0.988204S.D. dependent var18632.92S.E. of regression2023.722Akaike info criterion18.29822Sum squared resid40954495Schwarz criterion18.48081Log likelihood-124.0875F-statistic364.0187Durbin-Watson stat2.268874Prob(F-statistic)0.000000与，进行拟合Dependent Variable: YMethod: Least SquaresDate: 12/07/05 Time: 11:37Sample (adjusted): 1990 2003Included observations: 14 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.X10.7050400.04643615.183140.0000X41.3131790.2053996.3933010.0001X551.1000663.198120.8085690.4376C-124366.625463.52-4.8841080.0006R-squared0.991472Mean dependent var128929.8Adjusted R-squared0.988913S.D. dependent var18632.92S.E. of regression1961.952Akaike info criterion18.23622Sum squared resid38492570Schwarz criterion18.41881Log likelihood-123.6536F-statistic387.5140Durbin-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：3.3.2 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 Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/16/05 Time: 10:16Sample (adjusted): 1990 2003Included observations: 14 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C-1.73E+092.19E+09-0.7906190.4520X1-8308.1146576.899-1.2632270.2421X12-0.0098060.005714-1.7161540.1245X1*X40.0862850.0608041.4190800.1936X435696.1241501.400.8601190.4148X42-0.1844790.194738-0.9473160.3712R-squared0.582274Mean dependent var2929225.Adjusted R-squared0.321195S.D. dependent var3350446.S.E. of regression2760420.Akaike info criterion32.79719Sum squared resid6.10E+13Schwarz criterion33.07107Log likelihood-223.5803F-statistic2.230259Durbin-Watson stat2.734672Prob(F-statistic)0.149849表中结果显示，值较大，明显大于显著性水平，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 Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/07/05 Time: 10:35Sample (adjusted): 1993 2003Included observations: 11 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C2415514.1758278.1.3737950.2119RESID2(-1)-0.1111710.361472-0.3075510.7674RESID2(-2)0.3926290.3771841.0409480.3325RESID2(-3)0.2400180.4036340.5946430.5708R-squared0.201447Mean dependent var3653173.Adjusted R-squared-0.140790S.D. dependen