文響的計量經(jīng)濟分析2對我國能源消耗影響的計量經(jīng)濟分析GDP耗的計量模型，得析也可以看出中國的能源消耗和經(jīng)濟增長這些因素包括：能源的供給量，能源的需求量，還包括我國的人國經(jīng)濟快消耗的總量也越來越多，越來越快。石油危機、淡被減少、稀有物種滅絕等現(xiàn)象頻頻出現(xiàn)。有的國家慢、人口數(shù)量、生活習(xí)慣、人們的生活水平息內(nèi)生產(chǎn)總值(GDP)，國內(nèi)人口數(shù)量，能源價格。3Eviews了計建立t消費總量(單位：萬噸標(biāo)準(zhǔn)煤)；x：能源生產(chǎn)總量(單位：萬噸標(biāo)準(zhǔn)煤)；x：1t2t國內(nèi)生產(chǎn)總值(單位：億元)；x：我國的年底總?cè)丝跀?shù)；x：主要原材料、3t4ti根據(jù)引入的變量，設(shè)定模型為：y=b+bx+bx+bx+bx+u，t011t22t33t44tt其中u為隨機擾動項。t年份20002001能源消費總量能源生產(chǎn)總量國內(nèi)生產(chǎn)總我國的年底總?cè)丝谫忂M價格分類指數(shù)4200220032004200520062007模型的估計t1t2t3t4t4.1經(jīng)濟意義的檢驗，主要原材料、燃料、動力購進價格分類指數(shù)。a=0.05的條件下，t(13)=2.16，變量x,x的t統(tǒng)計量均沒有通過檢驗，認為a3t4t24.3計量經(jīng)濟學(xué)的檢驗以及模型的修正對于多重共線性3t4t3t4t5量分別與應(yīng)變量進行回歸,y與x，x，x，x進行擬合，結(jié)果見表4-6，得1t2t3t4t出x的R2最大。因此引入x作基本方程，在逐步引入x，x，x，在引入變t1t2t3t4t量時x，使其系數(shù)與經(jīng)濟意義相悖見表8，刪掉x，再引入x，其系數(shù)與經(jīng)3t3t4tt1t2t異方差的檢驗eaa6LULUd，d，UUUy10058.941.081211x0.075036xt1t2t可以看到，有的自變量回歸結(jié)果與實際意義不符合，有的自變量并沒有通過t檢力、鋼鐵、水泥、鋁、玻璃等耗能工業(yè)產(chǎn)品的需求，拉動了能源需求，又力、鋼鐵、水泥、電解鋁等耗能工業(yè)新上大項目的巨大動力，從而又推動7，調(diào)整經(jīng)濟結(jié)構(gòu)，完善產(chǎn)業(yè)發(fā)展政策，降低高耗能生產(chǎn)、生活、建筑等方面的節(jié)能措施，抑制我國能模型t1t2t不斷調(diào)整經(jīng)濟結(jié)構(gòu)，完善產(chǎn)業(yè)發(fā)展政策。當(dāng)前提高能【參考文獻】8【附錄】y20002001200220032004200520062007DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:30Sample:19902007Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C28021.0348855.890.5735450.5761X10.9236320.0967929.5424730.0000X20.2086010.0817292.5523560.0241X3-0.3650440.373525-0.9772930.3463X4152.898674.658082.0479850.0613R-squared0.998457Meandependentvar154036.3AdjustedR-squared0.997982S.D.dependentvar49161.80S.E.ofregression2208.216Akaikeinfocriterion18.46789Sumsquaredresid63390829Schwarzcriterion18.71522Loglikelihood-161.2110F-statistic2103.250Durbin-Watsonstat1.334173Prob(F-statistic)0.0000009X110.9745286602330.846798501996-020.97452866023310.929784102698-0.290596900051X3X40.846798501-0.158300730996330.929784102-0.2905969006980511-0.401107681572-0.4011076811572DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:35Sample:19902007Includedobservations:18VariableCoefficientStdErrort-StatisticC-21433.052415.379-8.8735770.0000X11.2083520.01605175.284140.0000R-squared0.997185Meandependentvar154036.3AdjustedR-squared0.997009S.D.dependentvar49161.80S.E.ofregression2688.664Akaikeinfocriterion18.73592Sumsquaredresid1.16E+08Schwarzcriterion18.83485Loglikelihood-166.6232F-statistic5667.702Durbin-Watsonstat1.121345Prob(F-statistic)0.000000DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:36Sample:19902007Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C81485.534485.73118.165500.0000X20.7232970.03758619.243950.0000R-squared0.958585Meandependentvar154036.3AdjustedR-squared0.955996S.D.dependentvar49161.80S.E.ofregression10312.72Akaikeinfocriterion21.42458Sumsquaredresid1.70E+09Schwarzcriterion21.52351Loglikelihood-190.8212F-statistic370.3298Durbin-Watsonstat0.304848Prob(F-statistic)0.000000DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:36Sample:19902007Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-777619.3141022.6-5.5141480.0000X37.4873611.1322556.6127860.0000R-squared0.732123Meandependentvar154036.3AdjustedR-squared0.715381S.D.dependentvar49161.80S.E.ofregression26227.68Akaikeinfocriterion23.29146Sumsquaredresid1.10E+10Schwarzcriterion23.39039Loglikelihood-207.6231F-statistic43.72895Durbin-Watsonstat0.179163Prob(F-statistic)0.000006DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:36Sample:19902007Includedobservations:18VariableCoefficientStdErrort-StatisticC243690.1141133.91.7266590.1035X4-836.36561312.010-0.6374690.5328R-squared0.024769Meandependentvar154036.3AdjustedR-squared-0.036183S.D.dependentvar49161.80S.E.ofregression50043.31Akaikeinfocriterion24.58360Sumsquaredresid4.01E+10Schwarzcriterion24.68253Loglikelihood-219.2524F-statistic0.406367Durbin-Watsonstat0.107152Prob(F-statistic)0.532836DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:38Sample:19902007Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-10004.095584.499-1.7914030.0934X11.0696090.06412916.679050.0000X20.0869180.0391522.2200480.0422R-squared0.997881Meandependentvar154036.3AdjustedR-squared0.997599S.D.dependentvar49161.80S.E.ofregression2409.118Akaikeinfocriterion18.56292Sumsquaredresid87057749Schwarzcriterion18.71132Loglikelihood-164.0663F-statistic3532.134Durbin-Watsonstat1.185443Prob(F-statistic)0.000000DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:38Sample:19902007Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C29443.1254137.790.5438550.5951X11.0203230.09364110.896170.0000X20.1399690.0826091.6943450.1123X3-0.3022690.412554-0.7326780.4758R-squared0.997959Meandependentvar154036.3AdjustedR-squared0.997522S.D.dependentvar49161.80S.E.ofregression2447.198Akaikeinfocriterion18.63640Sumsquaredresid83842872Schwarzcriterion18.83427Loglikelihood-163.7276F-statistic2282.222Durbin-Watsonstat1.194850Prob(F-statistic)0.000000DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:10:39Sample:19902007Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-19242.006923.754-2.7791280.0148X10.9865390.07217013.669590.0000X20.1422770.0454683.1291950.0074X4146.911274.286871.9776200.0680R-squared0.998344Meandependentvar154036.3AdjustedR-squared0.997989S.D.dependentvar49161.80S.E.ofregression2204.672Akaikeinfocriterion18.42768Sumsquaredresid68048108Schwarzcriterion18.62554Loglikelihood-161.8491F-statistic2813.036Durbin-Watsonstat1.271608Prob(F-statistic)0.000000DependentVariable:YMethod:LeastSquaresDate:01/04/10Time:14:48Sample:19902007Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-10004.095584.499-1.7914030.0934X11.0696090.06412916.679050.0000X20.0869180.0391522.2200480.0422R-squared0.997881Meandependentvar154036.3AdjustedR-squared0.997599S.D.dependentvar49161.80S.E.ofregression2409.118Akaikeinfocriterion18.56292Sumsquaredresid87057749Schwarzcriterion18.71132Loglikelihood-164.0663F-statistic3532.134Durbin-Watsonstat1.185443Prob(F-statistic)0.000000WhiteHeteroskedasticityTest:F-statisticObs*R-squared0.8317934.632807ProbabilityProbability0.5513210.462313TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:01/04/10Time:14:51Sample:19902007Includedobservations:18VariableCoefficientStdErrort-StatisticC1.32E+081.75E+080.7569200.4637X1-3655.7684499.648-0.8124560.4324X1^20.0253600.0291390.8703090.4012X1*X2-0.0362070.038421-0.9423790.3646X22627.3052960.9470.8873190.3923X2^20.0128440.0126701.0137270.3307R-squared0.257378Meandependentvar4836542.AdjustedR-squared-0.052048S.D.dependentvar5059566.S.E.ofregression5189565.Akaikeinfocriterion34.02340Sumsquaredresid3.23E+14Schwarzcriterion34.32019Loglikelihood-300.2106F-statistic0.831793Durbin-Watsonstat2.459745Prob(F-statistic)0.551321ARCHTest:F-statisticObs*R-squared0.5513481.960691ProbabilityProbability0.6576700.580605TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:01/04/10Time:14:53Sample(adjusted):19932007Includedobservations:15afteradjustingendpointsVariableCoefficientStdErrort-StatisticC3245810.2560794.1.2675020.2312RESID^2(-1)0.1137000.2846940.3993780.6973RESID^2(-2)-0.1523640.276952-0.5501470.5932RESID^2(-3)0.2481850.2311771.0735720.3060R-squared0.130713Meandependentvar4397292.AdjustedR-squared-0.106366S.D.dependentvar4403926.S.E.ofregression4632222.Akaikeinfocriterion33.75815Sumsquare