上海市能源消费量的计量分析

上海市能源消费量的计量分析一、前言在当今的国与国竞争中，能源问题已经成为非常重要的影响因素，在某种意义上说能源决定了一个国家的未来发展。能否获得有效能源保障关系到很多国家的生存和发展。在本文主要通过1985-2003年的一些数据，应用计量经济学的分析方法，来分析影响上海市的能源消费量的一些因素，并对未来的消费量进行粗略的预测。通过分析，在掌握和熟练计量分析方法的同时，对上海市的能源问题进行分析，并提出一些自己的看法。二、理论背景在近期，我国相继采取了一系列的解决未来能源问题的行动，例如：中国在巴西购买铝土，在智利购买铜，在澳大利亚购买锌，向哈萨克斯坦购买石油，与日本争夺“安大线”等等都表明能源对我国的重大意义。能否保证能源的供需平衡是我国未来的一个巨大挑战。而能源消费则成为此问题的重要因素。鉴于此我选择中国经济发展的一个代表—我国经济最发达城市上海作为分析的对象，分析影响能源消费量的因素，来探究我国能源问题的过去、现在和未来。三、模型建立与选择本文选择一些宏观因素从总体和宏观的角度来分析能源消费量的问题。本文选择了6个解释变量来解释能源消费量的变化，被解释变量能源消费量是总体的能源消耗量，包括多种能源。X1年末户籍人口表示人口数量，人口越多则对能源的需求量越多，消耗也越多。GDP表示上海市国内生产总值，是上海市经济规模和活跃程度的一个体现。用于教育的财政支出表示上海市对教育的主要投入，教育投入越多则表示人们素质就应该相对较高，科技人才就越多，科技水平就越高，相应能节省越多的能源。人均可支配收入表示人们平均所拥有的消费能力，人均可支配收入越多则能源的消费越多。全市财政收入主要包括全市企业所得和全市的税收收入，因此财政收入也表示上海市的经济规模和发展状况，财政收入越高则对能源的消费需求就越高。本文利用Eviews软件对每个解释变量的组合进行最小二乘法回归分析，建立一个合适有效的估计方程。然后，再利用夸特法对方程进行异方差检验，若存在异方差则利用加权最小二乘法进行修正。若DW检验值表示存在自相关则利用广义差分法对其修正。最终找到一个较准确有效的估计方程，继而对2004年上海市能源消费量进行预测。四、数据来源分析本文选择了6个解释变量（XI、X2、X3、X4、X5、X6）,来解释能源消费量（Y）其数据表格如下：能源消费量（万吨标准煤）Y年末户籍人口（万人）X1GDP（亿元）X2工业总产值（亿元）X3用于教育的财政支出（亿元）X4人均可支配收入（元）X5全市财政收入（亿元）X6消费价格指数（以1952年价格）19852553.211216.69466.75862.736.581075263.86134.819862783.661232.33490.83952.217.511293257.72143.319872868.061249.51545.461073.847.471437241.36154.919882980.641262.42648.31304.668.61723261.69186.0019893111.031276.45696.541524.6710.251975297.25215.6019903191.061283.35756.451642.7511.352182284.36229.2019913466.521287.2893.771947.1812.482486324.66253.2019923656.921289.371114.322429.9614.933009340.13278.5019933946.731294.741511.613327.0420.064277439.53334.8019944176.641298.811971.924255.1931.215868615.91414.82

19954465.871301.372462.575349.5339.447172702.46492.3919964626.211304.432902.25126.2249.898159873.76537.6919974758.821305.463360.215649.9363.5984391070.95552.7419984874.111306.583688.25763.6776.0187731146.00552.7419995119.191313.124034.966213.2484.27109321390.58561.0320005492.081321.634551.156968.1893.79117181752.70575.0020015818.281327.144950.847656.96111.06128831995.62575.0020026118.531334.235408.768476.05116.0713250.172202.25577.9020036697.61341.776250.8111266.62131.3714867.492828.87578.50以上所有数据的原始数据都得来源于上海统计局网站公布的上海2000-2004统计年鉴具体来源如下：能源消费量Y〜来源于15.1能源消耗基本情况（1985〜2003,是指上海市全市对于所有能源的消费量，主要包括煤、石油、天然气等能源的总消费量，衡量单位为万吨标准煤。年末户籍人口X1〜31历年户数、人口及人口密度,指上海市年末以户籍衡量的人口数。GDPX2〜21历年卜海市生产总值.上海市各年的国内生产总值。工业总产值X3〜112历年工业总产值及指数，上海市各年的工业生产总值。用于教育的财政支出X4〜94主要年份用于文教、卫生、科学部门的财政支出,上海市财政支出中对教育的支出。人均可支配收入X5〜416城市居民家庭人均可支配收入（1985〜2003,上海市居民人均可支配收入。全市财政收入X6〜91历年财政收入.上海市全市财政收入数额居民消费价格指数〜51历年居民消费价格指数,上海市历年的以1952年价格为基期价格计算的居民消费价格指数。下表为通过居民消费价格指数（以1952年价格为基期）调整为不变价格后的数据：年份能源消费量（万吨标准煤）Y年末户籍人口（万人）X1GDP（亿工业总产用于教冃的财人均可支配收入X5全市财政收入X6元）X2值X3政支出（亿元）X41985255321121669346256400148813797471957419862783661232333425266449524089023017985198728680612495135214693254822592770155821988298064126242348557014346237926341406919893111031276453230470718475429160513787199031910612833533004716734952952011240719913466521287235299769034928998183128221992365692128937400118725253609108043122131993394673129474451593745991612774813128199441766412988147537102587523814146014848199544658713013750013108645800991456571426619964626211304435397695338927861517421625019974758821305466072510221611504415267519375199848741113065866725104274137514158718207331999511919131312719211074615020519485424786200054920813216379151211861631132037913048220015818281327148610213316519314822405234706X2X2381.08381.08489.0020026118.531334.23935.931466.720.08482292.8120036697.61341.771080.51947.5622.70872570.01①①以上数据除人口外都已换算为不变价计量的数据。Y与X的散点图如下：X1X4X4YX3YX5X6五、模型的估计和分析对上面六个解释变量对被解释变量Y的所有组合作最小二乘法回归，结果如下：VariableCoefficientStd.Errort-StatisticProb.F-statistic六元回归C-6219.07002975.2830-2.0902450.0585518.8512X16.06282.42252.5027280.0278X23.37181.66462.0255370.0656X30.56730.42241.3429690.2041X419.640753.06130.3701510.7177

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X15.97512.91242.0516480.0594X31.52770.34964.369360.0006X4166.087523.96196.9313240.0000X6-4.79931.4242-3.3698150.0046C-8351.59203851.2580-2.1685360.0478368.3625X17.66173.21362.3841290.0318X30.45650.37901.2043060.2484X51.67750.29125.761210.0000X6-0.81051.1394-0.711370.4885C728.3924370.63921.9652330.0695144.2209X21.20043.69270.3250870.7499X30.19110.74070.258020.8001X4-65.6980104.6171-0.6279840.5401X52.30910.57933.98590.0014C1367.3460210.51726.4951740.0000455.2830X23.51792.00201.757220.1007X31.44870.42633.3985410.0043X498.071162.12041.5787260.1367X6-7.07270.7966-8.8782080.0000C996.6806-102.911796848150.00005343994X24.24271.11523.8043430.0019X30.79430.27862.8505580.0128X50.8803-0.38112.3100780.0366X6-5.46990.8825--6.1979460.0000C939.7948222.82314.2176720.0009366.6487X25.83071.79293.252080.0058X4-52.427047.3584-1.1070260.2869X51.14300.44442.571760.0222X6-4.89531.0620-4.6094520.0004C1363.6310169.15388.0614860.00005334250X31.492803208-46528630.0004X4128.806633.91833.7975580.0020X50.91480.37352.4491420.0281X6-5.78390.9474-6.1049410.0000C-10165.72003378.9490-3.0085450.0094379.8284X19.43762.78893.3839460.0045X448.268734.77391.3880720.18688.5263670.6137(L9943-$jOXCM00oXCM芟oXCMd2®回尺川®回尺川$SSCQJMMin0.00059-84061&2.157475-4.43628<£«CCdCL0_79血2364.9290■67(1少少U：jr■0Ca<c«&a-10491.4900O。尖殳芟。尖殳芟oSS◎£!胃関OSSX19.46281.94624.8621350.0002X30.30650.30980.9894730.3381X51.53440.20707.4111810.0000C-24804.09004583.2720-5.4118740.0001152.5971X121.35183.83755.5639810.0001X30.69830.66811.0451560.3125X63.72391.46132.5483720.0223C-11926.13002627.3790-4.5391740.0004516.2512X110.83122.22194.8747190.0002X431.408028.14211.116050.2820X51.29770.38153.4010550.0039C-17279.02003524.5480-4.9024790.0002290.4806X115.71762.78275.6482210.0000X4130.256733.44133.895090.0014X6-0.36371.4836-0.2451120.8097C-10477.01003474.0880-3.015760.0087476.3644X19.49252.87353.3034630.0048X51.71640.29375.844010.0000X6-0.04740.9611-0.0493650.9613C691.7005331.45272.0868750.0544205.0310X21.90242.41800.7867490.4437X4-84.742671.7962-1.1803220.2563X52.33080.55514.1988130.0008C1085.8370252.58814.2988430.0006354.1245X29.22381.42326.4811080.0000X4-55.836255.4960-1.006130.3303X6-6.47881.0144-6.3869330.0000C11582060104334711.10087000004812219X24.51151.34963.342807.0.0045-X51.15680.44762.5844130.0207X6-5.06931.0582-4.7906330.0002C810.1494264.01813.0685370.0078204.4487X30.36850.48570.758730.4598X4-34.253138.6505-0.8862280.3895X52.37000.53174.4574660.0005

C1663.1150134.952712323690.0000531.9707X32.07690.24788.3806^0.0000X4203.5353-17.105111.899130.0000X6-7.26460.8423-8.6252440.0000C826.4596127.68436.472680.0000372.8309X30.88390.38252.3107230.0355X52.19080.22469.7561070.0000X6-2.95070.8038-3.6709340.0023C1248.8030257.98164.8406690.0002296.2275X449.938245.29151.1025960.2876X52.20660.38525.7284050.0000X6-3.25361.1960-2.7204420.0158C995.4189118.01888.4344080.0000437.8304X52.59370.159616.25640.0000X6-2.32290.8529-2.7234020.0150C2620.8990165.651415.821780.0000143.1977X4286.409732.21340.0000X6-5.89861.9072-3.0927720.0070C857.0190253.26303.3839090.0038314.7321X4-37.027037.9635-0.9753320.3439X52.61120.42056.2104040.0000C673.7650332.54062.0261140.059874.2999X33.78170.63265.9781050.0000X6-0.90412.0362-0.4440070.6630X30.40920.48030.8520880.4067310.4442X51.97570.28926.8316770.0000C1499.7470315.83314.7485430.0002136.1616X31.66320.57472.8940520.0106X4109.901831.24553.517370.0029C1320.509096.983613.615790.0000530.2730X27.86130.437817.955070.0000X6-6.68470.9939-6.7255330.0000C1038.0730155.97496.6553840.0000299.4907X2-0.50861.3099-0.3882390.7030X52.42330.5562C893.7828468.3203X28.42162.6472X4-127.3237101.5018C-2868606002693.0250X124.67842.1500X65.0684^0.6952C-10599.190023608760X19.59421.9404X51.70320.1174C-17859.800025314350X116.16812.0270X4122.551811.0703C-18331.17004421.8270X115.78523.6570X32.19730.3673C-16032.71001945.7330X114.231515839-X23.45250.2256C1200.3220251.2173X23.84971.0028X30.94010.70724.3565460.00051.9084860.0744146.48183.1812780.0058-1.2543980.2277-10.651990.0000227.039011.478540.00007.2909190.0000-4.4895140.0004762.05794.9443640.0001145113900000-7.055210.0000462一88297.9761620.000011.070310.0000-4.145610.0008167.96994.3164760.00055.9813950.0000-8.23993-00000-842.37368.9853600000-1530400000-4.7780230.0002148.18153.8389440.00141.3292380.2024在所有通过t、F检验的估计结果中，我认为Y、XI、X2组合的回归方程具有最好的经济解释，故选择YX1X2组合模型（上表中加★的估计结果）进行分析。六、模型的估计和分析1、利用夸特法对YX1X2模型进行异方差检验：（通过Eviews软件）首先按X1升序排列Y、X1、X2的数据，去除中间3年（1993-1995）的数据，分别以1985-1992年数据和1996-2003年数据对YX1X2进行最小二乘法回归，得到198^1992年数据回归的ye2ye230263.5818-(2+1)18-(2+130263.5818-(2+1)18-(2+1)10941.565=2-76593<5-05=F。.。55,5)因此，认为本模型没有异方差性。2、自相关性检验：由于YX1X2模型的最小二乘估计结果中Durbin-Watsonstat=0.513917<1.08=dL(k=3,n=19),则认为此模型存在严重的

自相关性。3、解决自相关性：利用Eviews软件对YX1X2模型作一阶广义差分回归，得如下结果:VariableCoefficientStd.Errort-StatisticProb.C-4885.8198843.670-0.5524650.5893X16.1748657.1958410.8581160.4053X23.2687000.8506663.8425200.0018AR⑴0.9181800.1497676.1307100.0000R-squared0.995677Meandependentvar4341.775AdjustedR-squared0.994750S.D.dependentvar1190.096S.E.ofregression86.22653Akaikeinfocriterion11.94496Sumsquaredresid104090.2Schwarzcriterion12.14282Loglikelihood-103.5047F-statistic1074.801Durbin-Watsonstat1.722476Prob(F-statistic)0.000000上表中X1的t-Statistic=0.858116，Prob=0.4053>0.05(显著性水平)，因此模型还需调整。对模型进行二阶广义差分回归，结果如下：VariableCoefficientStd.Errort-StatisticProb.C-12216.861405.631-8.6913730.0000X111.263571.11239010.125560.0000X23.5477670.33067810.728770.0000AR(1)1.0582170.2634074.0174220.0017AR(2)-0.3519130.272399-1.2919030.2207R-squared0.996202Meandependentvar4376.651AdjustedR-squared0.994935S.D.dependentvar1235.339S.E.ofregression87.91391Akaikeinfocriterion12.03052Sumsquaredresid92746.26Schwarzcriterion12.27558Loglikelihood-97.25943F-statistic786.7995Durbin-Watsonstat1.661638Prob(F-statistic)0.000000InvertedARRoots.53+.27i.53-.27i结果中X1、X2的t-Statistic、Prob都通过，且Durbin-Watsonstat=1.661638e(1.53,3.92)=(dU,4dL)因此，本模型修正了自相关性，结果为：Y=-12216.86295+11.2635696*X1+3.547767047*X2+[AR(1)=1.058216645,AR(2)=-0.3519134989]然后对模型在进行异方差检验：分别以1987-1993年和1997-2003年数据为样本作最小二乘估计，其vei2估计，其vei2Sumsquaredresid)分别为：8574.197和914.1965e2'7-(2+1)8574.1975亠9・3789>6・39=F扁嶼)17-(2+1)因此，认为存在异方差，则进行以下修正：1、对原始数据除以YX1X2模型的resid序列的绝对值，得到如下数据:residY，X1，X2，

75.209333.9480616.177394.60381995.9570129.0094512.842523.569515-97.352629.4605512.83493.617162-156.10619.093668.0869252.232773-137.31122.656899.2960832.352624-179.64517.763137.1438071.837178-38.211190.720333.686579.237898-41.37588.3846831.162999.670322-5.41186729.274239.241183.4278584.1649649.6244515.431725.648075251.478617.758455.1748741.988758231.447819.988145.6359582.332102116.390340.8867311.216225.2173588.591222567.336152.083177.66648-18.7598272.880769.9964538.33728-16.5952330.944879.6395247.69465-8.82816659.0592150.330397.53108-68.105889.8385519.5905413.74229-95.537770.1042414.044411.30988这样，认为对原始数据消除了异方差性，然后对上表的YX1池进行最小二乘估计,结果如下:VariableCoefficientStd.Errort-StatisticProb.C-2.0574820.958671-2.1461830.0475X1/1.4928550.03720240.128480.0000池4.4637670.07756757.547060.0000R-squared0.999817Meandependentvar167.3017AdjustedR-squared0.999794S.D.dependentvar233.1169S.E.ofregression3.345195Akaikeinfocriterion5.396866Sumsquaredresid179.0452Schwarzcriterion5.545988Loglikelihood-48.27023F-statistic43698.63Durbin-Watsonstat0.592042Prob(F-statistic)0.000000由于DW=0.592042v1.08，则认为存在自相关性，则对YzX1ZX2‘进行一阶广义差分最小二乘估计，结果如下：VariableCoefficientStd.Errort-StatisticProb.C-0.3353371.051525-0.3189050.7545X11.5004980.02111571.064580.0000X24.4227800.04725993.585640.0000AR(1)0.5510130.1297714.2460420.0008R-squared0.999954Meandependentvar174.7102AdjustedR-squared0.999944S.D.dependentvar237.5626S.E.ofregression1.779056Akaikeinfocriterion4.183173Sumsquaredresid44.31058Schwarzcriterion4.381034Loglikelihood-33.64856F-statistic101037.9

Durbin-Watsonstat2.112105Prob(F-statistic)0.000000因为DW=2.112105G（1.53,3.92）=（du，4-dL），则认为不存在自相关性则最终结果为：Y＇=-0.3353365166+1.500497634*X1＇+4.422780226*X2＇[AR(1)=0.5510125247](-0.318905)(71.06458)(93.58564)统计意义：当X1不变时，X2'每增加（减少）一个单位时，Y平均增加（减少）4.422780226个单位。当X2Z不变时，X1Z每增加（减少）一个单位时，Y平均增加（减少）1.500497634个单位。经济意义：当人口数量不变时，GDP每增加（减少）一亿元，能源消费量增加（减少）4.422780226万吨标准煤。当GDP不变时，人口数量每增加（减少）一万人，能源消费量增加（减少）1.500497634万吨标准煤。统计分析：所需数据值如下：y=YY'x'=X'-X'x'=X'-X'i1i1i12i2i2工y'2=978182.9745工x'2=76081.54322工x'2=17500.57342i1i2i工y；x1广267560.4078Xy'x'=工y；x1广267560.4078（一）大©eta系数Xx'（一）大©eta系数Xx'21i=1.500497634：76081.54322'978182.97450.41847057统计意义：当X2‘保持不变时，每变化一个标准差，Y'平均变化0.41847057个标准差。经济意义：当GDP保持不变时，人口每变化以个标准差，能源消费量平均变化0.41847057个标准差。B*2—/X一BB*2—/X一Bx22i4.422780226：17500.57342978182.9745=0.591577229统计意义：当保持不变时，X2‘每变化一个标准差，Y'平均变化0.41847057个标准差。经济意义：当人口数量保持不变时，GDP每变化以个标准差，能源消费量平均变化0.591577229个标准差。由于，B*vB*,因此，GDP比人口数量对能源消费量的影响更为显著。12（二）偏相关系数ry'x'ry'x'1Xy'x'i1i267560.4078<978182.9745x76081.54322=0.980781223ry'x'2工y'x'i2i129615.8712工y'2工x'2=0.990653992<978182.9745x17500.57342rry'x'2工y'x'i2i129615.8712工y'2工x'2=0.990653992<978182.9745x17500.57342rx'x'12工x'x'1i2i34495.908991ox2ry'x'2口x'irx'2x'1口y工x'2工x'21i2irry'x'1ry'xI]=0.945369958<76081.54322x17500.57342y'x'2x'1x，2r—丄2x'x'12y'x'1x'1x，2卜-r2yrr一丄2x'x'_12)=rX'ix'2I]1-r2''丿Iyx2丿0.980780.99065X0.94537=0.994914017(1—0.990652)(1—0.945372)0.990650.98078X0.94537=0.997516721-0.980782)(1—0.945372)0.945370.99065X0.98078t=—0.985736661J(1—0.980782)(1—0.990652)三）弹性系数ax'1统计意义：经济意义：aYXax'1统计意义：经济意义：aYXXYX'x厂=1.500497634Y47.03239«0.4218167.3017空aX'2统计意义：经济意义：在X2＇在GDP保持不变时，当人口数量变化1%时，能源消费量平均变化42.18%X'入X'22.21143XA=BXA=4.422780226x«0.5872Y2Y167.3017保持不变时，当X1变化1%时，Y'平均变化42.18%。在X1保持不变时，当X2'变化1%时，Y'平均变化58.72%。在人口数量保持不变时，当GDP变化1%时，能源消费量平均变化58.72%。由于E<E，则GDP比人口数量对能源消费量的影响更为显著。12综上所述，弹性分析结果与大B系数分析结果一致，认为GDP对能源消费量的影响更为显著。七、预测（一）预测2004年X1,X值T和X2\T做最小二乘回归估计，得如下方程：=12.69382292+3.433856785*T=-3.173990152+2.538541551*T即2004年预测值为：X01＇=81.37095X02＇=47.59684使用EviewsT和X2\T做最小二乘回归估计，得如下方程：=12.69382292+3.433856785*T=-3.173990152+2.538541551*T即2004年预测值为：X01＇=81.37095X02＇=47.59684X1,X2,当t=20时（二）点预测2004年能源消费量预测值Y'为Y'=-0.3353365166+1.500497634*X1，+4.422780226*X2，0=-0.3353365166+1.500497634*81.37095+4.422780226*47.59684=332.2719442（万吨标准煤）

（三）区间预测1、均值预测：由YzX1‘X2‘模型估计得：。=237.5626a=0.0