计量经济学课程设计论文

1、计量经济学课程设计第 页共28页目录 TOC o 1-5 h z HYPERLINK l bookmark2 o Current Document 1引言 2 HYPERLINK l bookmark4 o Current Document 1 .1 金融业简介2 HYPERLINK l bookmark6 o Current Document 1 .2课题的意义2 HYPERLINK l bookmark8 o Current Document .3课题的内容和任务2 HYPERLINK l bookmark10 o Current Document 2建立模型及多元回归 2. 1模型建立

2、3 HYPERLINK l bookmark12 o Current Document 2 . 2多元回归 4 HYPERLINK l bookmark24 o Current Document 3回归模型的检验和预测 5 HYPERLINK l bookmark26 o Current Document 经济意义上变量的检验 5 HYPERLINK l bookmark28 o Current Document 3.1.1拟合优度检验 5 HYPERLINK l bookmark32 o Current Document 3.1.2对方程的T检验 53.1.3 对方程的 F检验 6 HYPE

3、RLINK l bookmark50 o Current Document 3.2计量经济学检验 63.2.1多重共线性检验 6 HYPERLINK l bookmark70 o Current Document 3.2.2异方差性检验 15 HYPERLINK l bookmark120 o Current Document 3.2.3序列相关性检验 20 HYPERLINK l bookmark158 o Current Document 3.3滞后变量模型 26 HYPERLINK l bookmark174 o Current Document 3.4模型参数的检验 27 HYPERL

4、INK l bookmark176 o Current Document 点估计 27 HYPERLINK l bookmark182 o Current Document 3.4.2区间估计 27 HYPERLINK l bookmark196 o Current Document 3.5模型的预测 2829 HYPERLINK l bookmark198 o Current Document 结论 29参考文献1引言1.1金融业简介金融业指的是银行与相关资金合作社， 还有保险业，除了工业性的经济行为 外，其他的与经济相关的都是金融业。 金融业是指经营金融商品的特殊行业， 它 包括银行业、保

5、险业、信托业、证券业和租赁业。随着WTO来临以及IT技术的日益精进，金融业已是二十一世纪的朝阳行业，在国家整个国民经济中处于牵一 发而动全身的地位，行业运转是否良好， 事关经济发展和社会稳定，具有优化资 金配置和调节、反映、监督经济的作用。1.2课题的意义从整个经济市场的发展趋势出发， 考察了货币供应量、上市公司数量、股票 的发行量、股票筹资额等对我国金融业的影响。因此，通过研究这些货币供应量、上市公司数量、股票的发行量、股票筹资 额等对我国金融业的影响，推动我国金融业的一体化、综合化、网络化发展的进程，优化了金融资源的合理配置，提高了竞争能力，改进了金融服务，降低了经 营成本，增加了盈利，对

6、发展中国金融业的竞争实力有很大的帮助。1.3课题内容和任务本文将在经济理论的指导下，采用计量经济的方法，并借助于计量经济学软 件Eviews，对我国的货币供应量、上市公司数量、股票的发行量和股票筹资额 等对金融业进行初步的实证分析。2建立模型及多元回归表一 1993年到2010年金融业及其影响因素的数据统计 上市金融业总 资产（亿货币供应量（亿元）黄金储 备（万 盎司）外汇储备（亿美公司数量（个股票的发 行量（亿股票筹资额（亿元）年份元）丫X1X2元）X3)X4股）X5X619931669.74534879.81267211.9918395.79375.4719942234.84446923.

7、51267516.229191.26326.7819952798.50360750.51267735.9732331.6150.3219963211.68576094.912671050.2953086.11425.0819973606.76290995.312671398.9745267.631293.8219983697.667104498.512671449.59851109.06841.5219993816.459119897.912671546.75949122.93944.5620004086.686134610.312671655.741088512.042103.2420014

8、353.456158301.916082121.651160141.481252.3420024612.80118500719292864.071224291.74961.7520034989.396221222.819294032.511287281.431357.7520045392.97525410719296099.321377227.921510.9420056086.826298755.719298188.721381567.051882.5120068099.082345603.6192910663.414341287.775594.29200712337.55403442.21

9、92915282.491550637.2409868025475166.6192919460.31625180.293852.21200917767.53606225338923991.521718415.966124.69201020980.63725774.1338928473.382063928.3711971.932.1模型建立根据统计数据表一金融业总资产模型，首先建立下面的模型：YXX X、 X 5 X XYt01X1t 2X2t 3X3t 4X4t 5X5t 6X6tt其中Yt是金融业总资产，Xit是货币供应量，X2t是黄金储备，X3t是外汇储备，X4t是

10、上市公司数量，X5t是股票的发行量，X6t是股票筹资额，S是常数项， (j =1,2,3)是待估参数，随机干扰项。通过Eviews软件，得到上述模型的散点图:X2X3 -X4 -X5X6YX1图1散点图2.2多元回归对于多元回归模型的建立，应该满足以下基本假设：解释变量是非随机的或固定的，且相互之间互不相关，即无多重共 线性。随机干扰项具有零均值，同方差及不序列相关性，即E(7) =0,i =1,2,，n Var(7) = E(叫)*2,i =1,2,，nCov(7,j)二 E(7，U)=0,i = j,i, j =1,2，n解释变量与随机干扰项不相关，即Cov(Xji,7) =0,j =1,

11、2,，k,i =1,2，n随机干扰项满足正态分布叫 N(0,；2)样本容量趋于无穷时，各解释变量的方差趋于有界常数。回归模型的设定是正确的。再助于计量经济学软件Eview3.1，对数据进行最小二乘估计结果如图所示:Depe ndent Variable: YMethod: Least SquaresDate: 06/01/12 Time: 20:40Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X1-0.0301830.023155-1.3035140.2190

12、X21.2390301.0836631.1433720.2772X31.0099080.3629652.7823840.0178X43.2316081.9401841.6656190.1240X5-1.3816850.864258-1.5986950.1382X60.2730280.1223992.2306310.0475C897.54541102.4220.8141570.4328R-squared0.994299Mean depe ndent var6922.547Adjusted R-squared0.991189S.D.dependent var5673.176S.E. of regr

13、essi on532.5272Akaike info criteri on15.67845Sum squared resid3119438.Schwarz criteri on16.02470Log likelihood-134.1060F-statistic319.7300Durb in -Watson stat1.950283Prob(F-statistic)0.000000图2最小二乘估计的回归结果估计模型结果如下：Yt =897.5454-0.030183X!t 1.239030X2( 1.009908乂氏 3.231608X4t -1.381685乂戢 0.273028X&0.814

14、157? ：i-1.3035141.1433722.7823841.665619-1.598695 ?2.2306312 2R =0.994299R =0.991189F =319.7300 S.E= 532.52723回归模型的检验和预测3.1经济意义上变量的检验3.1.1拟合优度检验从Eviews回归结果来看，模型拟合优度很好。R2 =0.9942993.1.2对方程的T检验1）对X1t进行检验提出原假设H0：Bj=O;备择假设 出：片式0 , （ j电 ）|t |=1.303514假设显著水平口 =0.05查自由度为11to.025（18-6-1）=2.201 1.303514，故接受

15、H。，拒绝 H）对X2t进行检验提出原假设H。： =0;备择假设已：=0，（ j假设显著水平a =0.05查自由度为11t.025（18-6-1）=2.201 =1.143372，故接受 H。，拒绝 H）对X3t进行检验提出原假设H。：打=0;备择假设 比：=0，（ j假设显著水平a =0.05查自由度为11to25（18-6-1）=2.201 2.782384,故拒绝原假设 H。， 著的。4 ）对X4t进行检验提出原假设H0: ： j = 0;备择假设 出：j = 0， （ j假设显著水平a =0.05查自由度为11to.o25（18-6-1）=2.201 1.665619,故接受 H。，拒

16、绝 H5）对X5t进行检验提出原假设H。：打=0;备择假设比：打=0，（ j假设显著水平口 =0.05查自由度为11to.o25（18-6-1）=2.201 1.598695,故接受 H。，拒绝 H6）X6t进行检验提出原假设Ho: 1 =0;备择假设H1j = 0，（ j假设显著水平口 =0.05查自由度为11to25（1861）=2.201 2.230631,故拒绝原假设 H。， 著的。的分布表，得临界值 1，即变量X1t是不显著的。=3）|t |=1.143372的分布表，得临界值 !，即变量X2t是不显著的。=2）|t |=2.782384的分布表，得临界值接受H1，即变量Xgt是显=

17、2）|t |=1.665619的分布表，得临界值，即变量X4t是不显著的。=2）|t |=1.598695的分布表，得临界值I,即变量X5t是不显著的。=2）|t |=2.230631的分布表，得临界值接受H1，即变量X6t是显3.1.3对方程的F检验F =319.7300假设显著水平:=0.05，查自由度为6和11的F分布表，得 临界值Fo.o5（6,11） = 5.O7v319.73O0是显然的，故F统计量的值在给定显著性水平 下=0.05的情况下是显著的。3.2计量经济学检验3.2.1多重共线性检验 计量经济学中多重共线性产生的原因：1）经济变量相关的共同趋势；2）滞后变量的引入；3）样

18、本资料的限制。 多重共线性的后果有：1）完全共线性下参数估计量不存在；2）近似共线性下普通最小二乘法参数估计量的方差变大；3）参数估计量经济含义不合理；4）变量的显著性检验和模型的预测功能失去意义。克服多重共线性的方法：1）排除引起共线性的变量；2）差分法；3）减小参数估计量的方差。 多重共线性的检验：1）检验多重共线性是否存在；2）判明存在多重共线性的范围（判定系数检验法、逐步回归法）。下列用相关系数检验法检验解释变量的多重共线性，经过计算得到变量 之间的相关系数如图所示：Correlation MatrixX1X2X3X4X5 | X6X1X2X3X4X5|X&X1xi1.0000000.

19、9317630.9066290.8901150.6102590.052538X2X20.9317631 0000000 9028060 8111560.5288840.787882X3X30 9866290 9028061 0000000 8258620.5647830 896160X4X4Q8961150 3111560.0258621 0000000 &373420771061X5X50.6182590.5288840 5&47830.6373421.0000000.73&453X6X60.S925380.7078820.896160077106107394531.000000图3相关系数

20、矩阵由上图知，相关系数在0.90以上，这说明解释变量之间高度线性相关，即 存在比较严重的多重共线性，也是货币供应量X1t与黄金储备X2t与外汇储备X3t之间存在比较严重的多重共线性。由于多重共线性的存在，我们采用逐步回归法对模型进行修正:第一步：运用OLS方法逐一求Y对各个解释变量的回归：Depe ndent Variable: YMethod: Least SquaresDate: 06/01/11 Time: 12:41Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticP

21、rob.X10.0277570.00142619.457960.0000C226.6330442.09700.5126320.6152R-squared0.959454Mean depe ndent var6922.547Adjusted R-squared0.956920S.D.dependent var5673.176S.E. of regressi on1177.512Akaike info criteri on17.08463Sum squared resid22184563Schwarz criteri on17.18356Log likelihood-151.7617F-stati

22、stic378.6123Durb in -Watson stat0.445939Prob(F-statistic)0.000000图4兀回归1Depe ndent Variable: YMethod: Least SquaresDate: 06/01/11Time: 12:44Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X27.6611220.9543138.0278950.0000C-6707.8661805.727-3.7147730.0019R-squa

23、red0.801112Mean depe ndent var6922.547Adjusted R-squared0.788681S.D. dependent var5673.176S.E. of regressi on2607.928Akaike info criteri on18.67494Sum squared resid1.09E+08Schwarz criteri on18.77387Log likelihood-166.0744F-statistic64.44709Durb in -Watson stat1.097939Prob(F-statistic)0.000001图5 一元回归

24、2Depe ndent Variable: YMethod: Least SquaresDate: 06/01/11Time: 12:50Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X30.6394200.01799135.541580.0000C2313.649201.440511.485520.0000R-squared0.987492Mean depe ndent var6922.547Adjusted R-squared0.986710S.D.depe

25、ndent var5673.176S.E. of regressi on654.0047Akaike info criteri on15.90855Sum squared resid6843554.Schwarz criteri on16.00748Log likelihood-141.1769F-statistic1263.204Durb in -Watson stat0.563353Prob(F-statistic)0.000000图6 -兀回归3Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 21:26Samp

26、le: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X48.8941801.5049555.9099310.0000C-2850.6751825.260-1.5617910.1379R-squared0.685827Mean depe ndent var6922.547Adjusted R-squared0.666191S.D.dependent var5673.176S.E. of regressi on3277.747Akaike info criteri on19.13

27、214Sum squared resid1.72E+08Schwarz criteri on19.23107Log likelihood-170.1893F-statistic34.92728Durb in -Watson stat0.163517Prob(F-statistic)0.000022图7 一元回归4Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12 Time: 21:27Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Error

28、t-StatisticProb.X59.0563533.6031632.5134450.0230C3765.0641714.6892.1957720.0432R-squared0.283071Mean depe ndent var6922.547Adjusted R-squared0.238263S.D.dependent var5673.176S.E. of regressi on4951.410Akaike info criteri on19.95717Sum squared resid3.92E+08Schwarz criteri on20.05610Log likelihood-177

29、.6145F-statistic6.317406Durb in -Watson stat0.527980Prob(F-statistic)0.023042图8 -兀回归5Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 21:28Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X61.5534630.1879948.2633800.0000C2637.633793.43363.324328

30、0.0043R-squared0.810164Mean depe ndent var6922.547Adjusted R-squared0.798300S.D.dependent var5673.176S.E. of regressi on2547.883Akaike info criteri on18.62835Sum squared resid1.04E+08Schwarz criteri on18.72728Log likelihood-165.6552F-statistic68.28345Durb in -Watson stat1.520117Prob(F-statistic)0.00

31、0000图9一元回归6第二步：对比分析，根据调整后的可决系数R2最大的原则，选取X3t作为进入回归模型的第一个解释变量，形成一元回归。再将其余解释变量分别引入模型， 得到二元回归模型如下：Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 21:48Sample: 1993 2010In eluded observati ons: 18VariableCoeffieie ntStd. Errort-StatistieProb.X30.6614550.1138585.8094590.0000X1-0.0009840.005

32、014-0.1961510.8471C2392.089450.65265.3080540.0001R-squared0.987524Mean depe ndent var6922.547Adjusted R-squared0.985861S.D.dependent var5673.176S.E. of regressi on674.5885Akaike info eriteri on16.01709Sum squared resid6826045.Sehwarz eriteri on16.16549Log likelihood-141.1539F-statistie593.6649Durb i

33、n -Watson stat0.556224Prob(F-statistie)0.000000图10二元回归1Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 21:49Sample: 1993 2010In eluded observati ons: 18VariableCoeffieie ntStd. Errort-StatistieProb.X30.6459970.04316514.965650.0000X2-0.0968980.574197-0.1687540.8682C2438.645769.31033.16

34、99110.0063R-squared0.987516Mean depe ndent var6922.547Adjusted R-squared0.985851S.D.dependent var5673.176S.E. of regressi on674.8128Akaike info eriteri on16.01776Sum squared resid6830585.Sehwarz eriteri on16.16615Log likelihood-141.1598F-statistie593.2652Durb in -Watson stat0.556326Prob(F-statistie)

35、0.000000图11二元回归2Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 21:49Sample: 1993 2010In eluded observati ons: 18VariableCoeffieie ntStd. Errort-StatistieProb.X30.6269430.03272019.160600.0000X40.2521630.5461330.4617240.6509C2126.498454.94084.6742300.0003R-squared0.987667Mean depe nden

36、t var6922.547Adjusted R-squared0.986023S.D.dependent var5673.176S.E. of regressi on670.7037Akaike info eriteri on16.00554Sum squared resid6747652.Schwarz eriteri on16.15394Log likelihood-141.0499F-statistie600.6491Durb in -Watson stat0.565155Prob(F-statistie)0.000000图12二元回归3Depe ndent Variable: YMet

37、hod: Least SquaresDate: 06/03/12Time: 21:50Sample: 1993 2010In eluded observati ons: 18VariableCoeffieie ntStd. Errort-StatistieProb.X30.6550000.02135930.665750.0000X5-0.7297490.565033-1.2915150.2161C2455.775225.969210.867740.0000R-squared0.988744Mean depe ndent var6922.547Adjusted R-squared0.987243

38、S.D.dependent var5673.176S.E. of regressi on640.7653Akaike info eriteri on15.91422Sum squared resid6158702.Sehwarz eriteri on16.06261Log likelihood-140.2279F-statistie658.8057Durb in -Watson stat0.683607Prob(F-statistie)0.000000图13二元回归4Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 2

39、1:50Sample: 1993 2010In eluded observati ons: 18VariableCoeffieie ntStd. Errort-StatistieProb.X30.6114410.04109114.880290.0000X60.0837410.1102140.7598050.4591C2284.336207.768710.994610.0000R-squared0.987956Mean depe ndent var6922.547Adjusted R-squared0.986350S.D. dependent var5673.176S.E. of regress

40、i on662.8186Akaike info criteri on15.98189Sum squared resid6589928.Schwarz criteri on16.13029Log likelihood-140.8370F-statistic615.2046Durb in -Watson stat0.492248Prob(F-statistic)0.000000图14二元回归5第三步：再根据调整后的可决系数R2最大原则和参数显著性原则，选取X5t 作为进入回归模型的第二个解释变量，形成二元回归；根据上面选取解释变量的原则，继续进行逐步回归，使得调整后的可决系数R2最大和参数都显著，

41、如图15和图16,依次引入解释变量X6t和X4t，最后引入的解释变量X1t和X2t，虽然 也使调整后的可决系数有所增加，但是它们的参数都不显著，如图17和图18,故舍去X1t和X2t，所以最后得到的回归模型是图16。Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12 Time: 22:13Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X30.5794040.03665815.805490.0000X

42、5-1.7047920.639190-2.6671130.0184X60.2885670.1205312.3941360.0312C2544.661200.487712.692360.0000R-squared0.992014Mean depe ndent var6922.547Adjusted R-squared0.990302S.D. dependent var5673.176S.E. of regressi on558.6763Akaike info criteri on15.68215Sum squared resid4369669.Schwarz criteri on15.88001

43、Log likelihood-137.1393F-statistic579.6653Durb in -Watson stat0.743211Prob(F-statistic)0.000000图15引入X6t回归Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 22:18Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X30.5398840.04191812.879540.0000X5-2.

44、1129180.650296-3.2491660.0063X60.3179940.1149382.7666640.0160X40.7761050.4664001.6640330.1200C2037.833358.40485.6858440.0001R-squared0.993416Mean depe ndent var6922.547Adjusted R-squared0.991390S.D. dependent var5673.176S.E. of regressi on526.4076Akaike info criteri on15.60016Sum squared resid360236

45、4.Schwarz criteri on15.84749Log likelihood-135.4015F-statistic490.3750Durb in -Watson stat1.176105Prob(F-statistic)0.000000图16引入X4t回归Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 22:19Sample: 1993 2010In cluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X30.65715

46、80.1936423.3936770.0053X41.3643731.0608571.2861040.2227X5-1.9356580.724783-2.6706740.0204X60.2984130.1219002.4480100.0307X1-0.0064110.010322-0.6210900.5462C2084.834374.90125.5610220.0001R-squared0.993621Mean depe ndent var6922.547Adjusted R-squared0.990963S.D. dependent var5673.176S.E. of regressi o

47、n539.3027Akaike info criteri on15.67963Sum squared resid3490169.Schwarz criteri on15.97642Log likelihood-135.1167F-statistic373.8411Durb in -Watson stat1.172971Prob(F-statistic)0.000000图17引入X1t回归Depe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 22:20Sample: 1993 2010In eluded observati

48、ons: 18VariableCoeffieie ntStd. Errort-StatistieProb.X30.5418280.0544129.9579440.0000X40.7835420.5010631.5637610.1438X5-2.1104720.677984-3.1128620.0090X60.3168760.1210662.6173760.0225X2-0.0293360.490696-0.0597850.9533C2070.071655.65413.1572610.0083R-squared0.993418Mean depe ndent var6922.547Adjusted

49、 R-squared0.990676S.D. dependent var5673.176S.E. of regressi on547.8208Akaike info eriteri on15.71098Sum squared resid3601292.Schwarz eriteri on16.00777Log likelihood-135.3988F-statistie362.2317Durb in -Watson stat1.175479Prob(F-statistie)0.000000图18引入X2t回归3.2.2异方差性检验计量经济学中异方差性检验的后果有：1）参数估计量非有效；2）变量

50、的显著性检验失去意义；3）模型的预测失效。异方差性检验的方法有：1）图示检验法;300002000010000E2图19解释变量与残差平方 e2的散点图1200000-800000-600000-400000-200000800000-0600000400000200000 1A0-200000-4000009496980002080406-100000010ResidualActualFitted图20残差e2由图3解释变量与残差平方e2的散点图和图4残差e2的信息都表明可能不存在异方差。2）帕克（Park）检验与戈里瑟（Gleiser ）检验;Depe ndent Variable: LO

51、G（E2）Method: Least SquaresDate: 06/03/12Time: 23:21Sample: 1993 2010In eluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.X3-5.01E-060.000138-0.0362770.9716X40.0004400.0015380.2862890.7792X50.0001560.0021440.0727840.9431X6-8.68E-050.000379-0.2290870.8224C10.973751.1817909.2857010.

52、0000R-squared0.014686Mean depe ndent var11.23634Adjusted R-squared-0.288487S.D.dependent var1.529145S.E. of regressi on1.735756Akaike info criteri on4.170897Sum squared resid39.16705Schwarz criteri on4.418222Log likelihood-32.53807F-statistic0.048442Durb in -Watson stat2.732721Prob(F-statistic)0.995

53、019图21 Park 检验给定显著水平a =0.05查自由度为13的分布表，得临界值 t.025(13) = 2.160|t|，所以不存在异方差。3)G-Q (Goldfeld-Quandt )检验;G-Q检验描述：将n =18组数据观察值按可能引起异方差的解释变量X1t的观察值进行升序排序；并将序列中间的c = n、4个观察值除去，并将剩下的观察值4划分为较小与较大的容量相同的两个子样本，每个子样样本容量均为口 = 7 ；4对每个子样分别进行OLS回归，并计算各自的残差平方和RSS和RSS。回归结果如下：Depe ndent Variable: YMethod: Least Squares

54、Date: 06/03/12 Time: 23:37Sample: 1993 1999In eluded observati ons: 7VariableCoefficie ntStd. Errort-StatisticProb.X60.1212410.2643580.4586260.6915X5-1.2946150.980986-1.3197080.3177X4-1.5312240.356560-4.2944300.0502X32.4535430.11857420.692110.0023C1503.48445.2998433.189610.0009R-squared0.999785Mean

55、depe ndent var3005.095Adjusted R-squared0.999354S.D.dependent var812.5859S.E. of regressi on20.65808Akaike info criteri on9.069898Sum squared resid853.5123Schwarz criteri on9.031263Log likelihood-26.74464F-statistic2320.366Durb in -Watson stat2.737001Prob(F-statistic)0.000431图 22 1993-1999年OLS回归结果De

56、pe ndent Variable: YMethod: Least SquaresDate: 06/03/12Time: 23:39Sample: 2004 2010In eluded observati ons: 7VariableCoefficie ntStd. Errort-StatisticProb.X60.2106030.0797682.6401900.1185X5-1.1856310.442025-2.6822750.1154X4-1.7521361.889538-0.9272830.4517X30.6954710.05015713.865960.0052C3392.3322207

57、.2601.5368970.2641R-squared0.999177Mean depe ndent var12218.26Adjusted R-squared0.997532S.D. dependent var5996.254S.E. of regressi on297.8897Akaike info criteri on14.40713Sum squared resid177476.5Schwarz criteri on14.36850Log likelihood-45.42496F-statistic607.2715Durb in -Watson stat3.192268Prob(F-s

58、tatistic)0.001645图 23 2004-2010年OLS回归结果计算样本1的残差平方和：RSS= 853.5123和样本2的残差平方和:RSS =177476.5给定显著性水平:=0.05，确定F分布表中的相应临界值 (2,2)=99.00 ；在同方差性假设下计算F的统计量：F二RS故表明存在RSS -z ss异方差。1）怀特（White）检验;White Heteroskedasticity Test:F-statistic1.362653Probability0.325854Obs*R-squared9.859796Probability0.275005Test Equati

59、 on:Dependent Variable: RESIDEMethod: Least SquaresDate: 06/03/12Time: 23:26Sample: 1993 2010In eluded observati ons: 18VariableCoefficie ntStd. Errort-StatisticProb.C345085.5360519.00.9571910.3635X6-39.10161220.9114-0.1770010.8634X6A2-0.0011700.016110-0.0725990.9437X51878.2541046.1631.7953740.1062X

60、5A2-1.3908590.645411-2.1549980.0596X4-1493.2521228.708-1.2153030.2552X4A20.9340780.8267491.1298210.2878X331.9191077.585730.4114040.6904X3A2-0.0025470.001827-1.3940790.1968R-squared0.547766Mean depe ndent var200131.4Adjusted R-squared0.145781S.D.dependent var281807.4S.E. of regressi on260457.5Akaike