计量经济学实验报告

word文档可自由复制编辑计量经济学实验报告计量经济学实验报告实验一实验目的：掌握一元线性回归模型的估计方法。实验要求：选择方程进行一元线性回归。实验原理：普通最小二乘法（OLS）实验数据：东莞市经济部分数据、广东省宏观经济部分数据。实验结果：1、REV作为应变量，把GDP作为解释变量DependentVariable:REVMethod:LeastSquaresDate:06/08/14Time:14:06Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.GDP0.0847810.00331125.604530.0000C-5826.1582517.475-2.3142860.0343R-squared0.976176Meandependentvar38637.72AdjustedR-squared0.974687S.D.dependentvar48603.38S.E.ofregression7732.823Akaikeinfocriterion20.84877Sumsquaredresid9.57E+08Schwarzcriterion20.94771Loglikelihood-185.6390F-statistic655.5922Durbin-Watsonstat0.335513Prob(F-statistic)0.000000得到了估计方程：REV=0.08478103497*GDP-5826.1578622、把EXB作为应变量，把REV作为解释变量DependentVariable:EXBMethod:LeastSquaresDate:06/08/14Time:14:09Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.REV0.7193080.01115364.497070.0000C-2457.310680.5738-3.6106440.0023R-squared0.996168Meandependentvar25335.11AdjustedR-squared0.995929S.D.dependentvar35027.97S.E.ofregression2234.939Akaikeinfocriterion18.36626Sumsquaredresid79919268Schwarzcriterion18.46519Loglikelihood-163.2963F-statistic4159.872Durbin-Watsonstat2.181183Prob(F-statistic)0.000000得到了估计方程：EXB=0.719308*REV-2457.301把SLC作为应变量，把GDP作为解释变量DependentVariable:SLCMethod:LeastSquaresDate:06/08/14Time:14:14Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.GDP0.4318270.004046106.72670.0000C-2411.3613076.237-0.7838670.4446R-squared0.998597Meandependentvar224062.6AdjustedR-squared0.998510S.D.dependentvar244763.3S.E.ofregression9449.149Akaikeinfocriterion21.24968Sumsquaredresid1.43E+09Schwarzcriterion21.34861Loglikelihood-189.2471F-statistic11390.59Durbin-Watsonstat1.715091Prob(F-statistic)0.000000得到了估计方程：SLC=0.4318268605*GDP-2411.36095LB作为应变量，GDP1作为解释变量DependentVariable:LBMethod:LeastSquaresDate:06/08/14Time:14:19Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.4965380.00527794.102520.0000C57.1378922.175202.5766570.0176R-squared0.997634Meandependentvar1486.166AdjustedR-squared0.997521S.D.dependentvar1556.667S.E.ofregression77.49819Akaikeinfocriterion11.62133Sumsquaredresid126125.4Schwarzcriterion11.72007Loglikelihood-131.6453F-statistic8855.284Durbin-Watsonstat1.781264Prob(F-statistic)0.000000得到了估计方程：LB=0.4965380887*GDP1+57.13788724ZJ作为应变量，GDP1作为解释变量DependentVariable:ZJMethod:LeastSquaresDate:06/08/14Time:14:23Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1654060.00294056.260930.0000C-42.9895112.35547-3.4793900.0022R-squared0.993409Meandependentvar433.0448AdjustedR-squared0.993095S.D.dependentvar519.6546S.E.ofregression43.18007Akaikeinfocriterion10.45158Sumsquaredresid39154.89Schwarzcriterion10.55032Loglikelihood-118.1931F-statistic3165.292Durbin-Watsonstat0.405988Prob(F-statistic)0.000000得到了估计方程：ZJ=0.1654055246*GDP1-42.9895126SE作为应变量，GDP1作为解释变量DependentVariable:SEMethod:LeastSquaresDate:06/08/14Time:14:29Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1664470.00345948.124650.0000C-36.6064514.53536-2.5184410.0200R-squared0.991014Meandependentvar442.4265AdjustedR-squared0.990586S.D.dependentvar523.5596S.E.ofregression50.79837Akaikeinfocriterion10.77655Sumsquaredresid54189.96Schwarzcriterion10.87529Loglikelihood-121.9303F-statistic2315.982Durbin-Watsonstat0.633700Prob(F-statistic)0.000000得到了估计方程：SE=0.1664474631*GDP1-36.60645462YY作为应变量，GDP1作为解释变量DependentVariable:YYMethod:LeastSquaresDate:06/08/14Time:14:31Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1716080.00616127.855130.0000C22.4615625.891060.8675410.3955R-squared0.973648Meandependentvar516.3478AdjustedR-squared0.972393S.D.dependentvar544.5861S.E.ofregression90.48441Akaikeinfocriterion11.93117Sumsquaredresid171936.0Schwarzcriterion12.02991Loglikelihood-135.2085F-statistic775.9083Durbin-Watsonstat1.099129Prob(F-statistic)0.000000由于常数项没有通过检验，所以去掉常数项重新检验。DependentVariable:YYMethod:LeastSquaresDate:06/08/14Time:14:38Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1752690.00446439.261420.0000R-squared0.972704Meandependentvar516.3478AdjustedR-squared0.972704S.D.dependentvar544.5861S.E.ofregression89.97426Akaikeinfocriterion11.87943Sumsquaredresid178098.1Schwarzcriterion11.92880Loglikelihood-135.6134Durbin-Watsonstat1.058314得到了估计方程：YY=0.1752685914*GDP1CS作为应变量，SE作为解释变量DependentVariable:CSMethod:LeastSquaresDate:06/08/14Time:14:43Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.SE0.4905460.01350936.311580.0000C29.031219.1419293.1756120.0046R-squared0.984323Meandependentvar246.0617AdjustedR-squared0.983576S.D.dependentvar258.8672S.E.ofregression33.17508Akaikeinfocriterion9.924416Sumsquaredresid23112.31Schwarzcriterion10.02316Loglikelihood-112.1308F-statistic1318.531Durbin-Watsonstat1.281007Prob(F-statistic)0.000000得到了估计方程：CS=0.4905459191*SE+29.03121437CZ作为应变量，CS作为解释变量DependentVariable:CZMethod:LeastSquaresDate:06/08/14Time:14:49Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.CS1.2620160.02737746.098370.0000C-20.247229.665329-2.0948300.0485R-squared0.990215Meandependentvar290.2865AdjustedR-squared0.989749S.D.dependentvar328.3047S.E.ofregression33.24050Akaikeinfocriterion9.928356Sumsquaredresid23203.55Schwarzcriterion10.02709Loglikelihood-112.1761F-statistic2125.060Durbin-Watsonstat1.475902Prob(F-statistic)0.000000得到了估计方程：CZ=1.26201555*CS-20.24721934实验二实验目的：掌握一元线性回归模型的检验方法。实验要求：进行经济、拟合优度、参数显著性和方程显著性等检验。实验原理：拟合优度的判定系数R2检验和参数显著性t检验等。实验结果：1.把REV作为应变量，把GDP作为解释变量REV=0.08478103497*GDP-5826.157862（0.003311）（2517.475）（25.60453）（-2.314286）R2=0.976176SE=7732.823REV对GDP的回归系数为0.08478103497，R2=0.976176，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量GDP在95%的置信度下显著，即通过变量显著性检验。2.把EXB作为应变量，把REV作为解释变量EXB=0.7193079524*REV-2457.309747(0.011153)(0.011153)(64.49707)(-3.610644)R2=0.996168SE=2234.939EXB对REV的回归系数为0.7193079524，R2=0.996168，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量REV在95%的置信度下显著，即通过变量显著性检验。3.把SLC作为应变量，把GDP作为解释变量SLC=0.4318268605*GDP-2411.36095(0.004046)(3076.237)(106.7267)(-0.783867)R2=0.998597SE=9449.149SLC对GDP的回归系数为0.4318268605，R2=0.998597，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量GDP在95%的置信度下显著，即通过变量显著性检验。4.把LB作为应变量，GDP1作为解释变量LB=0.4965380887*GDP1+57.13788724(0.005277)(22.17520)(94.10252)(2.576657)R2=0.997634SE=77.49819LB对GDP1的回归系数为0.4965380887，R2=0.997634，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量GDP1在95%的置信度下显著，即通过变量显著性检验。5.把ZJ作为应变量，GDP1作为解释变量ZJ=0.1654055246*GDP1-42.9895126(0.002940) (12.35547)(56.26093)(-3.479390)R2=0.993409SE=43.18007ZJ对GDP1的回归系数为0.1654055246，R2=0.993409，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量GDP1在95%的置信度下显著，即通过变量显著性检验。6.把SE作为应变量，GDP1作为解释变量SE=0.1664474631*GDP1-36.60645462(0.003459)(14.53536)(48.12465)(-2.518441)R2=0.991014SE=50.79837SE对GDP1的回归系数为0.1664474631，R2=0.991014，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量GDP1在95%的置信度下显著，即通过变量显著性检验。7.把YY作为应变量，GDP1作为解释变量YY=0.1752685914*GDP1(0.004464)(39.26142)R2=0.972704SE=89.97426YY对GDP1的回归系数为0.1752685914，R2=0.972704，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量GDP1在95%的置信度下显著，即通过变量显著性检验。8.CS作为应变量，SE作为解释变量CS=0.4905459191*SE+29.03121437(0.013509)(9.141929)(36.31158)(3.175612)R2=0.984323SE=33.17508CS对SE的回归系数为0.4905459191，R2=0.984323，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量SE在95%的置信度下显著，即通过变量显著性检验。9.CZ作为应变量，CS作为解释变量CZ=1.26201555*CS-20.24721934(0.027377)(9.665329)(46.09837)(-2.094830)R2=0.990215SE=33.24050CZ对CS的回归系数为1.26201555，R2=0.990215，接近1，因此拟合优度好。t（16）=2.12，ltl>t(16)，说明解释变量CS在95%的置信度下显著，即通过变量显著性检验。实验三实验目的：掌握多元线性回归模型的估计和检验方法。实验要求：选择方程进行多元线性回归。实验原理：普通最小二乘法（OLS）。实验结果：根据东莞数据选择第二产业增加值（GDP2）、固定资产净值（NKF2）和劳动者人数（LT2）的数据，把GDP2作为应变量，NKF2和LT2作为两个解释变量进行二元线性回归分析。1、作GDP2与NKF2、GDP2与LT2的散点图ScatGDP2NKF2ScatGDP2LT2DependentVariable:GDP2Method:LeastSquaresDate:06/15/14Time:14:12Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.NKF20.6982960.02151232.461030.0000C55714.2411197.584.9755620.0001R-squared0.985043Meandependentvar264711.6AdjustedR-squared0.984108S.D.dependentvar308327.3S.E.ofregression38868.85Akaikeinfocriterion24.07821Sumsquaredresid2.42E+10Schwarzcriterion24.17714Loglikelihood-214.7039F-statistic1053.718Durbin-Watsonstat1.306346Prob(F-statistic)0.000000得到了估计方程：GDP2=0.6982962506*NKF2+55714.24213DependentVariable:GDP2Method:LeastSquaresDate:06/15/14Time:14:18Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.LT22.7109800.4400036.1612740.0000C-431249.1120096.7-3.5908500.0024R-squared0.703491Meandependentvar264711.6AdjustedR-squared0.684959S.D.dependentvar308327.3S.E.ofregression173059.4Akaikeinfocriterion27.06510Sumsquaredresid4.79E+11Schwarzcriterion27.16403Loglikelihood-241.5859F-statistic37.96130Durbin-Watsonstat0.325257Prob(F-statistic)0.000014得到了估计方程：GDP2=2.71097983*LT2-431249.0779作GDP2与NKF2和LT2的二元回归DependentVariable:GDP2Method:LeastSquaresDate:06/15/14Time:14:23Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.NKF20.6293780.02971521.180110.0000LT20.3953140.1365112.8958380.0111C-25143.3329418.08-0.8546900.4062R-squared0.990406Meandependentvar264711.6AdjustedR-squared0.989127S.D.dependentvar308327.3S.E.ofregression32150.29Akaikeinfocriterion23.74524Sumsquaredresid1.55E+10Schwarzcriterion23.89364Loglikelihood-210.7072F-statistic774.2594Durbin-Watsonstat1.983844Prob(F-statistic)0.000000得到了估计方程：GDP2=0.6293775361*NKF2+0.3953138872*LT2-25143.332112、作LB与GDP1的一元回归DependentVariable:LBMethod:LeastSquaresDate:06/15/14Time:14:32Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.5058490.004310117.37030.0000R-squared0.996886Meandependentvar1486.166AdjustedR-squared0.996886S.D.dependentvar1556.667S.E.ofregression86.86456Akaikeinfocriterion11.80908Sumsquaredresid165999.9Schwarzcriterion11.85845Loglikelihood-134.8044Durbin-Watsonstat1.395510作LB与GDP1、T的二元回归；DependentVariable:LBMethod:LeastSquaresDate:06/15/14Time:14:39Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.4787580.00937651.063660.0000T9.0105162.8737003.1355110.0050R-squared0.997879Meandependentvar1486.166AdjustedR-squared0.997778S.D.dependentvar1556.667S.E.ofregression73.37653Akaikeinfocriterion11.51203Sumsquaredresid113066.4Schwarzcriterion11.61077Loglikelihood-130.3883Durbin-Watsonstat1.917391估计方程的判定系数R2分别接近1，参数显著性t检验值均大于2，方程显著性F检验显著。调整的判定系数为0.997778，比一元回归有明显改善。所以，得到了估计方程：LB=0.4787579113*GDP1+9.010515807*TZJ与GDP1的一元回归DependentVariable:ZJMethod:LeastSquaresDate:06/15/14Time:14:43Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1584000.00262860.271220.0000R-squared0.989610Meandependentvar433.0448AdjustedR-squared0.989610S.D.dependentvar519.6546S.E.ofregression52.96956Akaikeinfocriterion10.81982Sumsquaredresid61727.04Schwarzcriterion10.86919Loglikelihood-123.4279Durbin-Watsonstat0.278503作ZJ与GDP1、T的二元回归；DependentVariable:ZJMethod:LeastSquaresDate:06/15/14Time:14:53Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1811640.00435141.635220.0000T-7.5713031.333671-5.6770410.0000R-squared0.995901Meandependentvar433.0448AdjustedR-squared0.995706S.D.dependentvar519.6546S.E.ofregression34.05371Akaikeinfocriterion9.976696Sumsquaredresid24352.76Schwarzcriterion10.07543Loglikelihood-112.7320Durbin-Watsonstat0.637365估计方程的判定系数R2分别接近1，参数显著性t检验值均大于2，方程显著性F检验显著。调整的判定系数为0.995706，比一元回归有明显改善。所以，得到了估计方程：ZJ=0.1811640816*GDP1-7.571303452*T作SE与GDP1的一元回归DependentVariable:SEMethod:LeastSquaresDate:06/15/14Time:14:57Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1604820.00281057.114940.0000R-squared0.988300Meandependentvar442.4265AdjustedR-squared0.988300S.D.dependentvar523.5596S.E.ofregression56.63148Akaikeinfocriterion10.95351Sumsquaredresid70556.74Schwarzcriterion11.00288Loglikelihood-124.9654Durbin-Watsonstat0.523106作SE与GDP1、T的二元回归；DependentVariable:SEMethod:LeastSquaresDate:06/15/14Time:15:01Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1806280.00566531.882960.0000T-6.7006471.736460-3.8587970.0009R-squared0.993154Meandependentvar442.4265AdjustedR-squared0.992828S.D.dependentvar523.5596S.E.ofregression44.33846Akaikeinfocriterion10.50452Sumsquaredresid41283.89Schwarzcriterion10.60326Loglikelihood-118.8020Durbin-Watsonstat0.763060估计方程的判定系数R2分别接近1，参数显著性t检验值均大于2，方程显著性F检验显著。调整的判定系数为0.992828，比一元回归有明显改善。所以，得到了估计方程：SE=0.1806284457*GDP1-6.70064709*T作YY与GDP1的一元回归DependentVariable:YYMethod:LeastSquaresDate:06/15/14Time:15:15Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1752690.00446439.261420.0000R-squared0.972704Meandependentvar516.3478AdjustedR-squared0.972704S.D.dependentvar544.5861S.E.ofregression89.97426Akaikeinfocriterion11.87943Sumsquaredresid178098.1Schwarzcriterion11.92880Loglikelihood-135.6134Durbin-Watsonstat1.058314作YY与GDP1、T的二元回归DependentVariable:YYMethod:LeastSquaresDate:06/15/14Time:15:25Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.GDP10.1594490.01115514.294190.0000T5.2618073.4189951.5389920.1387R-squared0.975470Meandependentvar516.3478AdjustedR-squared0.974302S.D.dependentvar544.5861S.E.ofregression87.29999Akaikeinfocriterion11.85952Sumsquaredresid160047.1Schwarzcriterion11.95826Loglikelihood-134.3845Durbin-Watsonstat1.198126估计方程的判定系数R2分别接近1，参数t检验GDP1显著，方程显著性F检验显著。调整的判定系数为0.974302，比一元回归有明显改善。所以，得到了估计方程：YY=0.1594485548*GDP1+5.261806944*T实验四第一部分异方差模型的检验实验目的：掌握异方差模型的检验方法。实验要求：掌握图形法检验和Glejser检验。实验原理：图形法检验、Glejser检验。实验结果：1、ZJ对GDP1和T回归的残差趋势图和残差散点图。genre2=resid^2从图上看ZJ对GDP1和T回归的残差存在异方差。2、做对ZJ和GDP1回归的Glejser检验。genrE1=residlsabs(e1)cgdp1lsabs(e1)cgdp1^2lsabs(e1)cSQR(gdp1)lsabs(e1)c1/gdp1以上四个对gdp1回归得结果分别为：对gdp1DependentVariable:ABS(E1)Method:LeastSquaresDate:06/16/14Time:17:21Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.C20.718966.0638983.4167730.0026GDP10.0043870.0014433.0403050.0062R-squared0.305635Meandependentvar33.34424AdjustedR-squared0.272570S.D.dependentvar24.84736S.E.ofregression21.19219Akaikeinfocriterion9.028084Sumsquaredresid9431.287Schwarzcriterion9.126822Loglikelihood-101.8230F-statistic9.243454Durbin-Watsonstat1.148496Prob(F-statistic)0.006221对gdp12回归得结果为：DependentVariable:ABS(E1)Method:LeastSquaresDate:06/16/14Time:17:24Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.C24.836605.3297584.6599850.0001GDP1^24.82E-071.64E-072.9285480.0080R-squared0.289974Meandependentvar33.34424AdjustedR-squared0.256164S.D.dependentvar24.84736S.E.ofregression21.42984Akaikeinfocriterion9.050387Sumsquaredresid9643.999Schwarzcriterion9.149126Loglikelihood-102.0795F-statistic8.576393Durbin-Watsonstat1.132628Prob(F-statistic)0.008028从F检验来为来看整个模型不显著(3)对SQR(gdp1)回归得结果为：DependentVariable:ABS(E1)Method:LeastSquaresDate:06/16/14Time:17:29Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.C12.165158.5006121.4310910.1671SQR(GDP1)0.4640730.1584552.9287360.0080R-squared0.290001Meandependentvar33.34424AdjustedR-squared0.256191S.D.dependentvar24.84736S.E.ofregression21.42944Akaikeinfocriterion9.050350Sumsquaredresid9643.639Schwarzcriterion9.149088Loglikelihood-102.0790F-statistic8.577497Durbin-Watsonstat1.118705Prob(F-statistic)0.008024常数项不显著，去掉常数项再进行回归得结果为：DependentVariable:ABS(E1)Method:LeastSquaresDate:06/16/14Time:17:31Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.SQR(GDP1)0.6569810.0852537.7062760.0000R-squared0.220758Meandependentvar33.34424AdjustedR-squared0.220758S.D.dependentvar24.84736S.E.ofregression21.93392Akaikeinfocriterion9.056451Sumsquaredresid10584.13Schwarzcriterion9.105820Loglikelihood-103.1492Durbin-Watsonstat1.019181(4)对1/gdp1回归得结果为：DependentVariable:ABS(E1)Method:LeastSquaresDate:06/16/14Time:17:36Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.C39.308827.0213465.5984740.00001/GDP1-3994.3633218.612-1.2410200.2283R-squared0.068328Meandependentvar33.34424AdjustedR-squared0.023963S.D.dependentvar24.84736S.E.ofregression24.54784Akaikeinfocriterion9.322066Sumsquaredresid12654.53Schwarzcriterion9.420805Loglikelihood-105.2038F-statistic1.540131Durbin-Watsonstat0.869730Prob(F-statistic)0.228283从F检验来为来看整个模型不显著从四个回归的结果看，回归(4)不显著，(1)、（2）、(3)显著，比较(1)(3)不带常数项的回归,选择（3），方程为：ABS(E1)=0.6569810506*SQR(GDP1)即异方差的形式为：^2=(0.6569810506*SQR(GDP1))^2=0.4316241008*也即异方差的形式为：^2=σ^2*第二部分异方差模型的处理实验目的：掌握异方差模型的处理方法。实验要求：理解同方差性变换，掌握加权最小二乘法（WLS）。实验原理：加权最小二乘法（WLS）、同方差性变换和广义最小二乘法（GLS）。实验结果：1、已知ZJ对GDP1和T回归异方差的形式为：把作为权数来进行加权最小二乘法。得到回归结果为：DependentVariable:ZJMethod:LeastSquaresDate:06/22/14Time:14:28Sample:19782000Includedobservations:23Weightingseries:1/(GDP1)^1/6VariableCoefficientStd.Errort-StatisticProb.GDP10.1589580.00464634.210210.0000T-3.4691890.487355-7.1183960.0000WeightedStatisticsR-squared0.706997Meandependentvar87.15467AdjustedR-squared0.693044S.D.dependentvar13.22542S.E.ofregression7.327359Akaikeinfocriterion6.904049Sumsquaredresid1127.494Schwarzcriterion7.002787Loglikelihood-77.39656Durbin-Watsonstat0.384334UnweightedStatisticsR-squared0.987389Meandependentvar433.0448AdjustedR-squared0.986788S.D.dependentvar519.6546S.E.ofregression59.73045Sumsquaredresid74922.25Durbin-Watsonstat0.241779回归方程为：ZJ=0.1589575268*GDP1-3.469188776*T它与存在异方差是的如下估计方程明显不同：ZJ=0.1811640816*GDP1-7.571303452*T进行同方差性变换，然后回归实际上就是ZJ/（GDP1^(1/2)）对1/(GDP1^(1/2))和GDP1/(GDP1^(1/2))回归：genre3=ZJ/（GDP1^(1/2)）genre4=1/(GDP1^(1/2))genre5=GDP1/(GDP1^(1/2))DependentVariable:E3Method:LeastSquaresDate:06/22/14Time:14:30Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.E4-17.738564.670348-3.7981230.0011E50.1566320.00336446.559510.0000R-squared0.978268Meandependentvar6.472632AdjustedR-squared0.977233S.D.dependentvar5.024696S.E.ofregression0.758166Akaikeinfocriterion2.367113Sumsquaredresid12.07113Schwarzcriterion2.465852Loglikelihood-25.22180Durbin-Watsonstat0.243102观察残差趋势图：可以看出还是存在异方差，再改为ZJ/GDP1对1/GDP1和C回归DependentVariable:ZJ/GDP1Method:LeastSquaresDate:06/22/14Time:14:43Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.1/GDP1-8.3677171.920934-4.3560660.0003C0.1426390.00419034.038710.0000R-squared0.474676Meandependentvar0.130144AdjustedR-squared0.449660S.D.dependentvar0.019749S.E.ofregression0.014651Akaikeinfocriterion-5.525722Sumsquaredresid0.004507Schwarzcriterion-5.426983Loglikelihood65.54580F-statistic18.97531Durbin-Watsonstat0.155279Prob(F-statistic)0.000277观察这时的残差趋势图：显然，不存在异方差了，其估计方程为：ZJ/GDP1=-8.367717289*(1/GDP1)+0.1426386909变换为原估计方程为：ZJ=0.1426386909*GDP—8.367717289实验五上自相关模型的检验实验目的：掌握自相关模型的检验方法。实验要求：熟悉图形法检验和掌握DW检验。实验原理：图形检验法和DW检验法。实验结果：做出EXB对REV回归的残差趋势图和残差散点图genre3=resid从图上看，EXB对REV回归的残差存在自相关。2、做出SLC对GDP回归的残差趋势图和残差散点图genre5=resid从图上看，SLC对GDP回归的残差存在自相关。D—W检验EXB作为应变量，REV作为解释变量的回归结果DependentVariable:EXBMethod:LeastSquaresDate:06/29/14Time:14:03Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.REV0.7193080.01115364.497070.0000C-2457.310680.5738-3.6106440.0023R-squared0.996168Meandependentvar25335.11AdjustedR-squared0.995929S.D.dependentvar35027.97S.E.ofregression2234.939Akaikeinfocriterion18.36626Sumsquaredresid79919268Schwarzcriterion18.46519Loglikelihood-163.2963F-statistic4159.872Durbin-Watsonstat2.181183Prob(F-statistic)0.000000查表，n=18，k=2.dl=1.16，du=1.39,D.W=2.181183。du<D.W<4-du,所以不存在自相关性。SLC作为应变量，GDP作为解释变量的回归结果DependentVariable:SLCMethod:LeastSquaresDate:06/29/14Time:14:15Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.GDP0.4318270.004046106.72670.0000C-2411.3613076.237-0.7838670.4446R-squared0.998597Meandependentvar224062.6AdjustedR-squared0.998510S.D.dependentvar244763.3S.E.ofregression9449.149Akaikeinfocriterion21.24968Sumsquaredresid1.43E+09Schwarzcriterion21.34861Loglikelihood-189.2471F-statistic11390.59Durbin-Watsonstat1.715091Prob(F-statistic)0.000000查表，n=18，k=2.dl=1.16，du=1.39,D.W=1.715091。du<D.W<4-du,所以不存在自相关性。用DW检验，根据东莞数据LOG(REV)对T和GDP的回归结果DependentVariable:LOG(REV)Method:LeastSquaresDate:06/29/14Time:14:23Sample:19781995Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.T0.1181920.0144558.1766160.0000GDP8.62E-071.36E-076.3249060.0000C8.3813770.08905894.111600.0000R-squared0.983332Meandependentvar9.956133AdjustedR-squared0.981109S.D.dependentvar1.094994S.E.ofregression0.150500Akaikeinfocriterion-0.798701Sumsquaredresid0.339752Schwarzcriterion-0.650306Loglikelihood10.18831F-statistic442.4575Durbin-Watsonstat0.719654Prob(F-statistic)0.000000查表，n=18，k=3.dl=1.05，du=1.53,D.W=0.719654。0<D.W<dl,所以，存在正自相关。下自相关模型的处理实验目的：掌握自相关模型的处理方法。实验要求：理解广义差分变换和掌握迭代法。实验原理：广义差分变换、迭代法和广义最小二乘（GLS）。实验结果：LOG(REV)对T和GDP回归自相关的处理DependentVariable:LOG(REV)Method:LeastSquaresDate:06/29/14Time:14:29Sample(adjusted):19791995Includedobservations:17afteradjustingendpointsConvergenceachievedafter6iterationsVariableCoefficientStd.Errort-StatisticProb.C7.8096620.51724415.098600.0000GDP4.54E-072.31E-071.9684410.0707T0.1869930.0504613.7056660.0026AR(1)0.6719850.1792303.7492930.0024R-squared0.993388Meandependentvar10.02441AdjustedR-squared0.991862S.D.dependentvar1.088483S.E.ofregression0.098190Akaikeinfocriterion-1.601499Sumsquaredresid0.125337Schwarzcriterion-1.405448Loglikelihood17.61274F-statistic651.0664Durbin-Watsonstat1.549943Prob(F-statistic)0.000000InvertedARRoots.67D.W检验值也由0.719654提高到1.549943，也消除了自相关。没有消除和消除了自相关的回归方程分别为：LOG(REV)=8.38137739+8.617091912e-07*GDP+0.1181924057*TLOG(REV)=7.809662322+4.53726392e-07*GDP+0.1869934005*T+[AR(1)=0.6719853484]实验六上多重共线性模型的检验实验目的：掌握多重共线性模型的检验方法。实验要求：了解辅助回归检验和掌握R2值和t值检验及解释变量相关系数检验。实验原理：R2值和t值检验、解释变量相关系数检验和辅助回归检验。实验结果：在多元线性回归模型的估计和检验中，根据广东数据，建立固定资产投资模型，固定资产投资TZG取决于固定资产折旧ZJ、营业盈余YY和财政支出CZ，进行三元线性回归。DependentVariable:TZGMethod:LeastSquaresDate:06/29/14Time:14:44Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.C-34.6399840.25856-0.8604380.4003ZJ0.5053510.7701360.6561840.5196YY0.7504740.2039583.6795470.0016CZ1.2644521.0388741.2171380.2385R-squared0.991894Meandependentvar938.7587AdjustedR-squared0.990614S.D.dependentvar1082.535S.E.ofregression104.8795Akaikeinfocriterion12.30027Sumsquaredresid208994.5Schwarzcriterion12.49775Loglikelihood-137.4531F-statistic774.9422Durbin-Watsonstat1.523940Prob(F-statistic)0.000000从结果来看，判定系数R2=0.991894很高，方程很显著，但三个参数的t检验值只有一个较显著，很显然，存在多重共线性。2、解释变量相关系数检验可以看出，三个解释变量ZJ、YY和CZ之间高度相关，必然存在严重的多重共线性。根据广东数据，TZG对ZJ、YY和CZ的回归中，分别做解释变量ZJ、YY和CZ之间的辅助回归DependentVariable:ZJMethod:LeastSquaresDate:06/29/14Time:14:56Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.C-34.014598.875935-3.8322260.0010YY0.1701670.0453763.7501300.0013CZ1.3062750.07527017.354630.0000R-squared0.996878Meandependentvar433.0448AdjustedR-squared0.996566S.D.dependentvar519.6546S.E.ofregression30.45148Akaikeinfocriterion9.791254Sumsquaredresid18545.85Schwarzcriterion9.939362Loglikelihood-109.5994F-statistic3193.358Durbin-Watsonstat0.861064Prob(F-statistic)0.000000DependentVariable:YYMethod:LeastSquaresDate:06/29/14Time:15:04Sample:19782000Includedobservations:23VariableCoefficientStd.Errort-StatisticProb.ZJ2.4262060.6469663.7501300.0013C112.9