EViews軟件做的計(jì)量經(jīng)濟(jì)學(xué)實(shí)驗(yàn)關(guān)于能源消費(fèi)的論文

1、我國(guó)能源消費(fèi)影響因素計(jì)量分析一、 問(wèn)題的提出研究背景能源是經(jīng)濟(jì)增長(zhǎng)的戰(zhàn)略投入要素，在經(jīng)濟(jì)增長(zhǎng)初期，能源的投入能夠帶動(dòng)經(jīng)濟(jì)快速增長(zhǎng)。18世紀(jì)第一次工業(yè)革命，煤炭的燃燒推動(dòng)蒸汽機(jī)的普及，進(jìn)而帶動(dòng)了生產(chǎn)率的提高，實(shí)現(xiàn)了工業(yè)化的起步。隨著工業(yè)化進(jìn)程的深入，石油的大量使用成為經(jīng)濟(jì)持續(xù)增長(zhǎng)的推動(dòng)力量?？梢姡?jīng)濟(jì)增長(zhǎng)和能源投入之間形成了一定的互動(dòng)關(guān)系，能源是經(jīng)濟(jì)增長(zhǎng)的動(dòng)力源泉，經(jīng)濟(jì)增長(zhǎng)又拉動(dòng)能源消費(fèi)。研究目的我國(guó)國(guó)民經(jīng)濟(jì)在向工業(yè)化和現(xiàn)代化發(fā)展的進(jìn)程中，較長(zhǎng)時(shí)間處于能源消費(fèi)需求迅速增長(zhǎng)而供給不組的緊缺狀態(tài)，20世紀(jì)末的“九五”期間發(fā)生了顯著變化，能源生產(chǎn)和消費(fèi)總量均呈下降的趨勢(shì)，出現(xiàn)了難得的能源供需基本基本平

2、衡狀況，但同時(shí)也出現(xiàn)了新的問(wèn)題，即煤炭供過(guò)于求與石油的供不應(yīng)求的結(jié)構(gòu)性矛盾突出。本文擬從我國(guó)的能源消費(fèi)和生產(chǎn)入手，分析影響我國(guó)能源消費(fèi)與生產(chǎn)的主要因素，探討我國(guó)能源消費(fèi)的趨勢(shì)。1.3研究的相關(guān)理論支持及研究狀況劉鳳朝等于2007年9月發(fā)表了“中國(guó)經(jīng)濟(jì)增長(zhǎng)和能源消費(fèi)的動(dòng)態(tài)特征”一文。文章運(yùn)用基于向量自回歸模型的廣義預(yù)測(cè)誤差方差分解和廣義脈沖響應(yīng)分析方法，在資本，勞動(dòng)和能源三要素單部門新古典生產(chǎn)函數(shù)的框架內(nèi)，以中國(guó)19882005年間的能源消費(fèi)和經(jīng)濟(jì)增長(zhǎng)數(shù)據(jù)為樣本，考察了二者之件的動(dòng)態(tài)特征。結(jié)果顯示：在長(zhǎng)期，除了資本增長(zhǎng)外，經(jīng)濟(jì)增長(zhǎng)是能源消費(fèi)的重要增長(zhǎng)因素，貢獻(xiàn)度為14.92%。能源消費(fèi)增長(zhǎng)的沖擊

3、對(duì)經(jīng)濟(jì)增長(zhǎng)有正的影響作用。劉鳳朝、孫玉濤于2008年3月在中國(guó)人口.資源與環(huán)境上發(fā)表了“技術(shù)創(chuàng)新，產(chǎn)業(yè)結(jié)構(gòu)調(diào)整對(duì)能源消費(fèi)影響的實(shí)證分析”。指出，在產(chǎn)業(yè)結(jié)構(gòu)調(diào)整，減少能源效率的過(guò)程中，技術(shù)創(chuàng)新是關(guān)鍵因素。在現(xiàn)有的研究基礎(chǔ)上引入技術(shù)創(chuàng)新要素，建立技術(shù)創(chuàng)新，產(chǎn)業(yè)結(jié)構(gòu)調(diào)整對(duì)能源消費(fèi)影響的分析框架。通過(guò)假設(shè)建立了技術(shù)創(chuàng)新，產(chǎn)業(yè)結(jié)構(gòu)調(diào)整對(duì)能源消費(fèi)的計(jì)量經(jīng)濟(jì)模型，運(yùn)用中國(guó)的數(shù)據(jù)進(jìn)行了實(shí)證分析。研究結(jié)果表明，專利授權(quán)量增加能夠節(jié)約能源消費(fèi)，產(chǎn)業(yè)產(chǎn)值增加能夠減少能源消費(fèi)。研究結(jié)果認(rèn)為，產(chǎn)業(yè)結(jié)構(gòu)升級(jí)，優(yōu)化和經(jīng)濟(jì)增長(zhǎng)方式轉(zhuǎn)變，是經(jīng)濟(jì)增長(zhǎng)和能源消費(fèi)脫鉤的重要途徑。再找點(diǎn)能源產(chǎn)出的理論支持 而對(duì)于產(chǎn)業(yè)結(jié)構(gòu)的影響因素，錢

4、納里和賽爾奎因在發(fā)展型式，1950-1970一書中，設(shè)計(jì)了一個(gè)國(guó)民生產(chǎn)總值的市場(chǎng)占有率模型，在模型中，錢納里和賽爾奎因以人均國(guó)民生產(chǎn)總值和人口數(shù)量作為外生變量，用回歸方程對(duì)樣本國(guó)家的數(shù)據(jù)進(jìn)行計(jì)算，得到產(chǎn)業(yè)結(jié)構(gòu)演進(jìn)的“標(biāo)準(zhǔn)結(jié)構(gòu)”,xi=ln0+1lny+2(lny)2+3lnn其中xi是第i產(chǎn)業(yè)的粗附加價(jià)值的市場(chǎng)占有率，y是人均國(guó)內(nèi)生產(chǎn)總值，n是樣本國(guó)家的人口數(shù)量。二、 模型設(shè)定理論上認(rèn)為影響能源消費(fèi)需求總量的因素主要有經(jīng)濟(jì)發(fā)展水平、產(chǎn)業(yè)發(fā)展、能源生產(chǎn)總量、人口總數(shù)等，三、 數(shù)據(jù)的收集年份能源消費(fèi)標(biāo)準(zhǔn)煤總量y/萬(wàn)噸國(guó)內(nèi)生產(chǎn)總值x2/億元工業(yè)增加值x3/億元建筑業(yè)增加值x4/億元交通運(yùn)輸郵電業(yè)增加

5、值x5/億元人均電力消費(fèi)x6/千瓦時(shí)能源加工轉(zhuǎn)換效率x7/%19857668290163448.7417.9406.921.368.2919868085010275.23967525.7475.623.268.3219878663212058.64585.8665.8544.926.467.4819889299715042.85777.281066131.266.5419899693416092.3648479478635.366.5119909870318667.86858859.41147.542.467.2199110378321781.58087.11015.11409.746.965

6、.9199210917026923.510284.514151681.854.666199311599335333.9141882266.52205.661.267.32199412273748197.919480.72964.72898.372.765.2199513117660793.724950.63728.83424.183.571.05199613894871176.629447.64387.44068.593.171.519971377987897332921.44621.64593101.869.23199813221484402.334018.44985.85278.4106.

7、669.44199913383189677.135861.55172.15821.8118.269.19200013855399214.64003.65522.37333.4132.469.042001143199109655.243580.65931.78406.1144.669.032002151797120332.747431.36465.593930.4156.369.042003174990135822.854945.57490.810098.4173.769.42004203227159878.3652108694.312147.6190.270.71200522331918308

8、4.876912.910133.810526.1216.771.082006246270211923.591310.911851.112481.1249.471.242007265583249529.9107367.214014.114604.1274.971.25四、 模型的估計(jì)與調(diào)整（一）參數(shù)估計(jì)1、雙擊“eviews”，進(jìn)入主頁(yè)。輸入數(shù)據(jù)：點(diǎn)擊主菜單中的file/open /ev workfileexcel多重共線性的數(shù)據(jù).xls ；2、在ev主頁(yè)界面的窗口，輸入“l(fā)s y c x2 x3 x4 x5 x6 x7”，按“enter”.出現(xiàn)ols回歸結(jié)果,圖2：dependent vari

9、able: ymethod: least squaresdate: 11/01/10 time: 11:34sample: 1985 2007included observations: 23variablecoefficientstd. errort-statisticprob.  c168326.2108641.01.5493810.1408x2-0.1422900.763550-0.1863530.8545x30.5031080.2485522.0241570.0600x48.29423710.431120.7951430.4382x5-0.2030370.11101

10、9-1.8288410.0861x6233.9125388.51880.6020620.5556x7-1373.3761588.868-0.8643730.4002r-squared0.980436    mean dependent var139364.6adjusted r-squared0.973099    s.d. dependent var51705.05s.e. of regression8480.388    akaike info criterion21.1

11、7469sum squared resid1.15e+09    schwarz criterion21.52028log likelihood-236.5089    f-statistic133.6365durbin-watson stat1.380303    prob(f-statistic)0.000000由此可見，該模型的可決系數(shù)為0.995，修正的可決系數(shù)為0.993，模型擬和很好，f統(tǒng)計(jì)量為701.47，模型擬和很好，回歸方程整體上顯著。但是當(dāng)=0.05時(shí)，=

12、2.069，不僅x4、x5、x6、x7的系數(shù)t檢驗(yàn)不顯著，而且x2、x4、x6系數(shù)的符號(hào)與預(yù)期相反，這表明很可能存在嚴(yán)重的多重共線性。（即除了農(nóng)業(yè)增加值、工業(yè)增加值外，其他因素對(duì)財(cái)政收入的影響都不顯著，且農(nóng)業(yè)增加值、建筑業(yè)增加值、最終消費(fèi)的回歸系數(shù)還是負(fù)數(shù)，這說(shuō)明很可能存在嚴(yán)重的多重共線性。）（二）多重共線性的診斷與修正3、計(jì)算各解釋變量的相關(guān)系數(shù)：在workfile窗口，選擇x2、x3、x4、x5、x6、x7數(shù)據(jù)，點(diǎn)擊“quick”group statisticscorrelationsok,出現(xiàn)相關(guān)系數(shù)矩陣，如圖3：圖3： 相關(guān)系數(shù)矩陣x2x3x4x5x6x7x210.9657320838

13、866270.9986857898553320.345345026254410.9972578885234110.743816435340805x30.96573208388662710.9655924019815920.3239441524405460.9565951672026850.719495371382113x40.9986857898553320.96559240198159210.3298546529731210.9948853467152340.755788639731427x50.345345026254410.3239441524405460.329854652973121

14、10.3663218904310660.205555717546146x60.9972578885234110.9565951672026850.9948853467152340.36632189043106610.72634236114161x70.7438164353408050.7194953713821130.7557886397314270.2055557175461460.726342361141611由相關(guān)系數(shù)矩陣可以看出，各解釋變量相互之間的相關(guān)系數(shù)較高，特別是農(nóng)業(yè)增加值、工業(yè)增加值、建筑業(yè)增加值、最終消費(fèi)之間，相關(guān)系數(shù)都在0.8以上。這表明模型存在著多重共線性。1、采用逐步回

15、歸法，去檢驗(yàn)和解決多重共線性問(wèn)題。分別作y對(duì)x2、x3、x4、x5、x6、x7的一元回歸，結(jié)果如下圖4：在ev主頁(yè)界面的窗口，輸入“l(fā)s y c x2”，“回車鍵”。依次如上推出x3、x4、x5、x6、x7的一元回歸。綜上所述，結(jié)果如下圖4：圖4.一元回歸估計(jì)結(jié)果變量參數(shù)估計(jì)值0.7348351.66548113.190880.737886678.005819332.30t統(tǒng)計(jì)量25.3151718.0256525.963631.29452922.422944.7024270.9682710.9392930.9697890.0739030.9599070.5129060.9667600.936

16、4020.9683500.0298030.9579980.4897112、其中，加入 的最大，以 為基礎(chǔ)，順次加入其他變量逐步回歸。結(jié)果如下圖5：圖5.加入新變量的回歸結(jié)果（一）變量x2x3x4x5x6x7x2,x30.5326640.4817370.970921（5.092034）（2.001247）x2,x40.14876510.526020.966883（0.263146）（1.038045）x2,x50.754738-0.209480.970869（26.06504）（-1.99037）x2,x60.947069-197.21250.965588（2.373029）（-0.533247

17、）x2,x70.754414-951.4780.965709（17.10268）（-0.596733）（三）異方差的診斷與修正該模型樣本回歸估計(jì)式的書寫形式為：y = 11.44213599 + 0.6267829962*x (3.629253) (0.019872) t= 3.152752 31.54097 s.e.=9.158900 dw=1.597946 f=994.8326（一）圖形法1、在“workfile”頁(yè)面：選中x,y序列，點(diǎn)擊鼠標(biāo)右鍵，點(diǎn)擊openas groupyes2、在“group”頁(yè)面：點(diǎn)擊viewgraphscattersimple scatter, 得到x,y的散

18、點(diǎn)圖（圖3所示）：2、goldfeld-quandt法進(jìn)行檢驗(yàn)。a.將樣本x按遞增順序排序，去掉中間1/4的樣本，再分為兩個(gè)部分的樣本，即n1=n2=9。dependent variable: ymethod: least squaresdate: 11/01/10 time: 11:07sample: 1 9included observations: 9variablecoefficientstd. errort-statisticprob.  c-15.152720.772901-19.605000.0000x0.0002108.01e-0626.285140.000

19、0r-squared0.989970    mean dependent var5.000000adjusted r-squared0.988537    s.d. dependent var2.738613s.e. of regression0.293208    akaike info criterion0.577264sum squared resid0.601798    schwarz criterion0.621092log

20、 likelihood-0.597687    f-statistic690.9084durbin-watson stat1.352108    prob(f-statistic)0.000000dependent variable: ymethod: least squaresdate: 11/01/10 time: 11:08sample: 1 9included observations: 9variablecoefficientstd. errort-statisticprob.  c8

21、.8510300.9919968.9224450.0000x5.42e-055.14e-0610.537710.0000r-squared0.940700    mean dependent var19.00000adjusted r-squared0.932228    s.d. dependent var2.738613s.e. of regression0.712943    akaike info criterion2.354299sum squared resid3

22、.558014    schwarz criterion2.398127log likelihood-8.594347    f-statistic111.0434durbin-watson stat0.632734    prob(f-statistic)0.000015b.分別對(duì)兩個(gè)部分的樣本求最小二乘估計(jì)，得到兩個(gè)部分的殘差平方和，即= 0.601798 ， = 3.558014求f統(tǒng)計(jì)量為 f= = 5.912306給定，查f分布表，得臨界值為 = 3.79c.比較臨

23、界值與f統(tǒng)計(jì)量值，有f=5.912306 =3.79 ，說(shuō)明該模型的隨機(jī)誤差項(xiàng)存在異方差。修正異方差在運(yùn)用加權(quán)最小二乘法估計(jì)過(guò)程中，分別選用了權(quán)數(shù)=1/，=1/，=1/。1、在“workfile”頁(yè)面：點(diǎn)擊“generate”，輸入“w1=1/x”ok ；同樣的輸入“w2=1/x2”“w3=1/sqr(x)”；2、在“equation”頁(yè)面：點(diǎn)擊“estimate equation”,輸入“y c x”,點(diǎn)擊“weighted”,輸入“w1”,出現(xiàn)如圖6：dependent variable: ymethod: least squaresdate: 11/01/10 time: 12:31sa

24、mple: 1985 2007included observations: 23weighting series: w1variablecoefficientstd. errort-statisticprob.  c75342.481955.93038.520020.0000x20.8614960.0873089.8673060.0000weighted statisticsr-squared0.986045    mean dependent var102600.2adjusted r-squared0.985380 &#

25、160;  s.d. dependent var77372.86s.e. of regression9355.386    akaike info criterion21.20823sum squared resid1.84e+09    schwarz criterion21.30697log likelihood-241.8947    f-statistic97.36373durbin-watson stat0.269103  &

26、#160; prob(f-statistic)0.000000unweighted statisticsr-squared0.925702    mean dependent var139364.6adjusted r-squared0.922164    s.d. dependent var51705.05s.e. of regression14425.26    sum squared resid4.37e+09durbin-watson stat0.14180

27、3dependent variable: ymethod: least squaresdate: 11/01/10 time: 12:33sample: 1985 2007included observations: 23weighting series: w2variablecoefficientstd. errort-statisticprob.  c61583.222022.13930.454490.0000x21.8677210.17376210.748730.0000weighted statisticsr-squared0.998194  &

28、#160; mean dependent var89007.62adjusted r-squared0.998108    s.d. dependent var134618.1s.e. of regression5855.843    akaike info criterion20.27121sum squared resid7.20e+08    schwarz criterion20.36995log likelihood-231.1189  

29、;  f-statistic115.5353durbin-watson stat0.389451    prob(f-statistic)0.000000unweighted statisticsr-squared-3.468653    mean dependent var139364.6adjusted r-squared-3.681446    s.d. dependent var51705.05s.e. of regression111872.4&

30、#160;   sum squared resid2.63e+11durbin-watson stat0.023839dependent variable: ymethod: least squaresdate: 11/01/10 time: 12:34sample: 1985 2007included observations: 23weighting series: w3variablecoefficientstd. errort-statisticprob.  c79134.392101.45237.657000.0000x20.7416

31、510.04145717.889810.0000weighted statisticsr-squared0.892994    mean dependent var117941.4adjusted r-squared0.887898    s.d. dependent var26545.81s.e. of regression8887.964    akaike info criterion21.10572sum squared resid1.66e+09 

32、0;  schwarz criterion21.20446log likelihood-240.7158    f-statistic320.0453durbin-watson stat0.251182    prob(f-statistic)0.000000unweighted statisticsr-squared0.968188    mean dependent var139364.6adjusted r-squared0.966673 

33、   s.d. dependent var51705.05s.e. of regression9439.115    sum squared resid1.87e+09durbin-watson stat0.303249用權(quán)數(shù)的估計(jì)結(jié)果為： = 75342.48 + 0.861496 （38.52002） （9.867306）= 0.986045 dw= 0.269103 f=97.36373括號(hào)中的數(shù)據(jù)為t統(tǒng)計(jì)量值。由上可以看出，運(yùn)用加權(quán)最小二乘法消除了異方差后，參數(shù)的t檢驗(yàn)顯著，可決系數(shù)提高了不少，f檢驗(yàn)也顯著，并說(shuō)明銷

34、售收入每增長(zhǎng)1元，銷售利潤(rùn)平均增長(zhǎng)0.861496元。這說(shuō)明在其他因素不變的情況下，當(dāng)國(guó)民收入每上升1%時(shí)，能源消費(fèi)就平均增加0.23585%。dependent variable: ymethod: least squaresdate: 11/01/10 time: 12:31sample: 1985 2007included observations: 23weighting series: w1variablecoefficientstd. errort-statisticprob.  c75342.481955.93038.520020.0000x20.861496

35、0.0873089.8673060.0000weighted statisticsr-squared0.986045    mean dependent var102600.2adjusted r-squared0.985380    s.d. dependent var77372.86s.e. of regression9355.386    akaike info criterion21.20823sum squared resid1.84e+09  

36、  schwarz criterion21.30697log likelihood-241.8947    f-statistic97.36373durbin-watson stat0.269103    prob(f-statistic)0.000000unweighted statisticsr-squared0.925702    mean dependent var139364.6adjusted r-squared0.922164 &#

37、160;  s.d. dependent var51705.05s.e. of regression14425.26    sum squared resid4.37e+09durbin-watson stat0.141803dependent variable: ymethod: least squaresdate: 11/01/10 time: 12:52sample: 1985 2007included observations: 23variablecoefficientstd. errort-statisticprob.&#

38、160; c79687.883069.32225.962700.0000x20.7348350.02902725.315170.0000r-squared0.968271    mean dependent var139364.6adjusted r-squared0.966760    s.d. dependent var51705.05s.e. of regression9426.750    akaike info criterion21.22343sum s

39、quared resid1.87e+09    schwarz criterion21.32217log likelihood-242.0695    f-statistic640.8577durbin-watson stat0.304578    prob(f-statistic)0.000000n=23 , k=1 , dl=1.257 du=1.437 dw=0.269103 正相關(guān)dependent variable: et1method: least squaresdate: 11/01/10 time: 12:57sample (adjusted): 1986 2007included observations: 22 after adjustmentsvariablecoefficientstd. errort-statisticprob.  c1162.3071751.6980.6635320.5150x2-0.0074520.016204-0.4598630.6508et1(-1)0.8247850.1188416.9402200.0000r-squared0.717966