EVIEWS在计量经济学教学过程中的演示示例.doc

EVIEWS在计量经济学教学过程中的演示示例陈冬冬（川农经管）EVIEWS在计量经济学教学过程中的演示示例（一）目的：1、正确使用EVIEWS 2、会使用OLS和WLS，Goldfeld-Quandt检验 3、能根据计算结果进行异方差分析和出现异方差性后的补救。 3、数据为demo data1实例：某市人均储蓄与人均收入的关系分析（异方差性检验及补救）根据某市19781998年人均储蓄与人均收入的数据资料（见下表），其中X为人均收入（元），Y为人均储蓄（元），经分析人均储蓄受人均收入的线性影响，可建立一元线性回归模型进行分析。obsXY1978590.2000107.00001979664.9400123.00001980809.5000159.00001981875.5400189.00001982991.2500233.000019831109.950312.000019841357.870401.000019851682.800522.000019861890.580664.000019872098.250871.000019882499.5801033.00019892827.7301589.00019903084.1702209.00019913462.7102878.00019923932.5203722.00019935150.7905350.00019947153.3508080.00019959076.85011758.00199610448.2115839.00199711575.4818196.00199812500.8420954.001、用OLS估计法估计参数设模型为：运行EVIEWS软件，并输入数据，得计算结果如下：Dependent Variable: YMethod: Least SquaresDate: 10/11/05 Time: 23:10Sample: 1978 1998Included observations: 21VariableCoefficientStd. Errort-StatisticProb. C-2185.998339.9020-6.4312620.0000X1.6841580.06216627.091500.0000R-squared0.974766 Mean dependent var4533.238Adjusted R-squared0.973438 S.D. dependent var6535.103S.E. of regression1065.086 Akaike info criterion16.86989Sum squared resid21553736 Schwarz criterion16.96937Log likelihood-175.1338 F-statistic733.9495Durbin-Watson stat0.293421 Prob(F-statistic)0.0000002、异方差检验 （1）Goldfeld-Quandt检验在Procs菜单项选Sort series项，出现排序对话框，输入X，OK。在Sample菜单里，将时间定义为19781985，用OLS方法计算得如下结果：Y = -145.441495 + 0.3971185479*X（-8.730234） （25.42693）R-squared0.990805 Sum squared resid115.12284Dependent Variable: YMethod: Least SquaresDate: 10/11/05 Time: 23:25Sample: 1978 1985Included observations: 8VariableCoefficientStd. Errort-StatisticProb. C-145.441516.65952-8.7302340.0001X0.3971190.01561825.426930.0000R-squared0.990805 Mean dependent var255.7500Adjusted R-squared0.989273 S.D. dependent var146.0105S.E. of regression15.12284 Akaike info criterion8.482607Sum squared resid1372.202 Schwarz criterion8.502468Log likelihood-31.93043 F-statistic646.5287Durbin-Watson stat1.335534 Prob(F-statistic)0.000000在Sample菜单里，将时间定义为19911998，用OLS方法计算得如下结果：Y = -4602.367144 + 1.952519317*X（-5.065962） （18.40942）R-squared0.982604 Sum squared resid25811189.Dependent Variable: YMethod: Least SquaresDate: 10/11/05 Time: 23:29Sample: 1991 1998Included observations: 8VariableCoefficientStd. Errort-StatisticProb. C-4602.367908.4882-5.0659620.0023X1.9525190.10606118.409420.0000R-squared0.982604 Mean dependent var10847.12Adjusted R-squared0.979705 S.D. dependent var6908.102S.E. of regression984.1400 Akaike info criterion16.83373Sum squared resid5811189. Schwarz criterion16.85359Log likelihood-65.33492 F-statistic338.9068Durbin-Watson stat0.837367 Prob(F-statistic)0.000002求F统计量：，查F分布表，给定显著性水平，得临界值，比较，拒绝原假设，表明随机误差项显著的存在异方差。3、异方差的修正（1）WLS估计法。首先生成权函数,然后用OLS估计参数，Y = -2262.639946 + 1.566910934*XDependent Variable: YMethod: Least SquaresDate: 10/12/05 Time: 08:07Sample: 1978 1998Included observations: 21Weighting series: WVariableCoefficientStd. Errort-StatisticProb. C-2262.640131.2507-17.239070.0000X1.5669110.05763727.185900.0000Weighted StatisticsR-squared0.961501 Mean dependent var2183.201Adjusted R-squared0.959475 S.D. dependent var2104.209S.E. of regression423.5951 Akaike info criterion15.02583Sum squared resid3409224. Schwarz criterion15.12530Log likelihood-155.7712 F-statistic474.5211Durbin-Watson stat0.354490 Prob(F-statistic)0.000000Unweighted StatisticsR-squared0.962755 Mean dependent var4533.238Adjusted R-squared0.960794 S.D. dependent var6535.103S.E. of regression1293.978 Sum squared resid31813191Durbin-Watson stat0.224165（2）对数变换法。用GENR生成LY和LX序列，用OLS方法求LY 对LX的回归，结果如下：LY = -6.839135503 + 1.787148637*LXDependent Variable: LYMethod: Least SquaresDate: 10/12/05 Time: 00:05Sample: 1978 1998Included observations: 21VariableCoefficientStd. Errort-StatisticProb. C-6.8391360.237565-28.788450.0000LX1.7871490.03003359.506800.0000R-squared0.994663 Mean dependent var7.195082Adjusted R-squared0.994382 S.D. dependent var1.746173S.E. of regression0.130880 Akaike info criterion-1.138677Sum squared resid0.325463 Schwarz criterion-1.039199Log likelihood13.95611 F-statistic3541.059Durbin-Watson stat0.642916 Prob(F-statistic)0.000000比较方法（1）和（2），可以看出X与Y在对数线性回归下拟合效果较好。原因是Y的曲线呈对数型图形有关。EVIEWS在计量经济学教学过程中的演示示例（二）目的：1、正确使用EVIEWS 2、能根据计算结果进行多重共线性检验和出现多重共线性时的补救。 3、数据为demo data2实例：我国钢材供应量分析（多重共线性检验及补救） 通过分析我国改革开放以来（19781997）钢材供应量的历史资料，可以建立一个单一方程模型。根据理论及对现实情况的认识，影响我国钢材供应量Y（万吨）的主要因素有：原油产量X1（万吨），生铁产量X2（万吨），原煤产量X3（万吨），电力产量X4（亿千瓦小时），固定资产投资X5(亿元)，国内生产总值X6(亿元)，铁路运输量X7（万吨）。obsX1X2X3X4X5X6X7Y1978104053479.006.812566668.723624.1110119220819791061536736.352820699.364038.2111893249719801059538026.23006746.94517.8111279271619811012234176.223093638.214862.4107673267019821021235516.663277805.95294.7113495292019831060737387.153514885.265934.5118784307219841146140017.8937701052.437171124074337219851249048348.7241071523.518964.4130709369319861306950648.9444951795.3210202.2135635405819871341455039.2849732101.6911962.5140653438619881370557049.854522554.8614928.31449484689198913764582010.5458482340.5216909.21514894859199013831623810.86212253418547.91506815153199114099676510.8767753139.0321617.81528935638199214210758911.1675394473.7626638.11576276697199314524895611.583956811.3534634.41626637716199414608974112.492819355.3546759.41630938428199515004.9510529.2713.6110070.310702.9758478.11658558979199615733.3910722.513.9710813.112185.7967884.61688039338199716074.1411511.4113.7311355.5313838.9674772.41697349978设模型的函数形式为：一、运用OLS估计法对上式中参数进行估计，EVIEWS操作步骤为：1、 在FILE菜单中选择NEWWORKFILE，输入起止时间。2、 在主窗口菜单选QUICKEMPTY GROUP，在编辑数据区输入Y X1 X2 X3 X4 X5 X6 X7所对应的数据。3、 在主窗口菜单选在QUICKESTIMATE EQUATION，对参数做OSL估计，输出结果见下表：VariableCoefficientStd. Errort-StatisticProb. C139.2362718.24930.1938550.8495X1-0.0519540.090753-0.5724830.5776X20.1275320.1324660.9627510.3547X3-24.2942797.48792-0.2492030.8074X40.8632830.1867984.6214750.0006X50.3309140.1055923.1338890.0086X6-0.0700150.025490-2.7467550.0177X70.0023050.0190870.1207800.9059R-squared0.999222 Mean dependent var5153.350Adjusted R-squared0.998768 S.D. dependent var2511.950S.E. of regression88.17626 Akaike info criterion12.08573Sum squared resid93300.63 Schwarz criterion12.48402Log likelihood-112.8573 F-statistic2201.081Durbin-Watson stat1.703427 Prob(F-statistic)0.000000Y = 139.2361608 - 0.05195439459*X1 + 0.1275320853*X2 - 24.294272*X3 + 0.8632825292*X4 + 0.330913843*X5 - 0.07001518918*X6 + 0.002305379405*X7二、分析由F=2201.081F0.05(7,12)=2.91（显著性水平a=0.05），表明模型从整体上看钢材供应量与解释变量之间线性关系显著。三、检验计算解释变量之间的简单相关系数。EVIEWS过程如下：1、 主菜单QUICKGROUP STATISTICSCORRRELATION，在对话框中输入X1 X2 X3 X4 X5 X6 X7，结果如下：X1X2X3X4X5X6X7X1 1.000000 0.921956 0.975474 0.931882 0.826401 0.845837 0.986815X2 0.921956 1.000000 0.964400 0.994921 0.969686 0.972530 0.931689X3 0.975474 0.964400 1.000000 0.974809 0.894963 0.913344 0.982943X4 0.931882 0.994921 0.974809 1.000000 0.959613 0.969105 0.945444X5 0.826401 0.969686 0.894963 0.959613 1.000000 0.996169 0.827643X6 0.845837 0.972530 0.913344 0.969105 0.996169 1.000000 0.846079X7 0.986815 0.931689 0.982943 0.945444 0.827643 0.846079 1.0000002、由上表可以看出，解释变量之间存在高度线性相关性。尽管方程整体线性回归拟合较好，但X1 X2 X3 X7变量的参数t值并不显著， X3 X6 系数的符号与经济意义相悖。表明模型确实存在严重的多重共线性。四、修正1、运用OLS方法逐一求Y对各个解释变量的回归。结合经济意义和统计检验选出拟合效果最好的一元线性回归方程。VariableCoefficientStd. Errort-StatisticProb. C-10123.781528.060-6.6252500.0000X11.1817840.11693610.106290.0000R-squared0.850171 Mean dependent var5153.350Adjusted R-squared0.841847 S.D. dependent var2511.950S.E. of regression998.9623 Akaike info criterion16.74595Sum squared resid17962663 Schwarz criterion16.84552Log likelihood-165.4595 F-statistic102.1371Durbin-Watson stat0.217842 Prob(F-statistic)0.000000VariableCoefficientStd. Errort-StatisticProb. C-618.7199108.3930-5.7081160.0000X20.9262120.01601957.820170.0000R-squared0.994645 Mean dependent var5153.350Adjusted R-squared0.994347 S.D. dependent var2511.950S.E. of regression188.8610 Akaike info criterion13.41454Sum squared resid642032.9 Schwarz criterion13.51411Log likelihood-132.1454 F-statistic3343.172Durbin-Watson stat0.962290 Prob(F-statistic)0.000000VariableCoefficientStd. Errort-StatisticProb. C-3770.942581.6642-6.4830230.0000X3926.717858.3853715.872430.0000R-squared0.933317 Mean dependent var5153.350Adjusted R-squared0.929612 S.D. dependent var2511.950S.E. of regression666.4367 Akaike info criterion15.93641Sum squared resid7994483. Schwarz criterion16.03598Log likelihood-157.3641 F-statistic251.9341Durbin-Watson stat0.477559 Prob(F-statistic)0.000000VariableCoefficientStd. Errort-StatisticProb. C-34.3247491.75324-0.3740980.7127X40.8840470.01414662.493810.0000R-squared0.995412 Mean dependent var5153.350Adjusted R-squared0.995157 S.D. dependent var2511.950S.E. of regression174.8044 Akaike info criterion13.25985Sum squared resid550018.2 Schwarz criterion13.35942Log likelihood-130.5985 F-statistic3905.476Durbin-Watson stat0.824221 Prob(F-statistic)0.000000VariableCoefficientStd. Errort-StatisticProb. C2896.350211.024513.725180.0000X50.5724510.03698315.478920.0000R-squared0.930123 Mean dependent var5153.350Adjusted R-squared0.926241 S.D. dependent var2511.950S.E. of regression682.2088 Akaike info criterion15.98319Sum squared resid8377359. Schwarz criterion16.08276Log likelihood-157.8319 F-statistic239.5971Durbin-Watson stat0.181794 Prob(F-statistic)0.000000VariableCoefficientStd. Errort-StatisticProb. C2720.664205.340513.249520.0000X60.1086650.00656816.545350.0000R-squared0.938303 Mean dependent var5153.350Adjusted R-squared0.934875 S.D. dependent var2511.950S.E. of regression641.0376 Akaike info criterion15.85869Sum squared resid7396725. Schwarz criterion15.95827Log likelihood-156.5869 F-statistic273.7485Durbin-Watson stat0.259927 Prob(F-statistic)0.000000VariableCoefficientStd. Errort-StatisticProb. C-9760.0991317.227-7.4095820.0000X70.1068260.00932611.455240.0000R-squared0.879375 Mean dependent var5153.350Adjusted R-squared0.872673 S.D. dependent var2511.950S.E. of regression896.3356 Akaike info criterion16.52915Sum squared resid14461517 Schwarz criterion16.62872Log likelihood-163.2915 F-statistic131.2225Durbin-Watson stat0.183657 Prob(F-statistic)0.000000经分析在7个一元回归模型中钢材供应量Y对电力产量X4的线性关系强，拟合度好，即：Y = -34.32474492 + 0.8840472792*X4 （-0.374098） （62.49381）R2= 0.995412 S.E.=174.8044，F=3905.476截距项不显著，去掉，重新估计：Y = 0.8792594492*X42、逐步回归。将其余解释变量逐一代入上式，得如下模型：Y = -0.005935225118*X1 + 0.8906555628*X4 （-0.604681） （45.03888） R2= 0.995469 S.E.=173.7270， F=3954.290式中X1不显著，删去，继续：Y = 0.1741981867*X2 + 0.6978252624*X4 （1.879546） （7.217200） R2= 0.996135 S.E.=160.4431， F=4639.290Y = 0.2753793175*X2 + 0.5595511241*X4 + 0.04060861466*X5 （3.082485） （5.637333） （2.615818）R2= 0.997244 S.E.=139.4060， F=3075.985Y = 0.466836912*X2 + 0.5219953469*X4 - 0.03080496295*X5 - 0.004998894793*X7（3.245804） （5.366654） （-0.674009） （-1.651391）R2= 0.997646 S.E.=132.8222， F=2259.899X7不符合经济意义，应去掉。所以：Y = 0.2753793175*X2 + 0.5595511241*X4 + 0.04060861466*X5 （3.082485） （5.637333） （2.615818）R2= 0.997244 S.E.=139.4060， F=3075.985即为最优模型。Dependent Variable: YMethod: Least SquaresDate: 10/17/05 Time: 22:53Sample: 1978 1997Included observations: 20VariableCoefficientStd. Errort-StatisticProb. X20.2753790.0893373.0824850.0068X40.5595510.0992585.6373330.0000X50.0406090.0155242.6158180.0181R-squared0.997244 Mean dependent var5153.350Adjusted R-squared0.996920 S.D. dependent var2511.950S.E. of regression139.4060 Akaike info criterion12.85014Sum squared resid330378.5 Schwarz criterion12.99950Log likelihood-125.5014 F-statistic3075.985Durbin-Watson stat0.790639 Prob(F-statistic)0.000000EVIEWS在计量经济学教学过程中的演示示例（三）目的：1、正确使用EVIEWS 2、能根据计算结果进行序列相关性检验和补救。 3、数据为demo data3实例：国内生产总值和出口总额之间的关系分析（序列相关性检验及补救）根据某地区19781998年国内生产总值与出口总额的数据资料，其中X表示国内生产总值（人民币亿元），Y表示出口总额（人民币亿元）。试建立一元线性回归函数。设模型函数形式为：obsXY1978 3624.100 134.80001979 4038.200 139.70001980 4517.800 167.60001981 4860.300 211.70001982 5301.800 271.20001983 5957.400 367.60001984 7206.700 413.80001985 8989.100 438.30001986 10201.40 580.50001987 11954.50 808.90001988 14922.30 1082.1001989 16917.80 1470.0001990 18598.40 1766.7001991 21622.50 1956.0001992 26651.90 2985.8001993 34560.50 3827.1001994 46670.00 4676.3001995 57494.90 5284.8001996 66850.50 10421.801997 73142.70 12451.801998 78017.80 15231.701、用OLS估计方法求模型的参数估计值点击NEWWORKFILE，输入X,Y的数据。点击QUICKESITMATE EQUATION，在对话框中输入Y C X，结果如下：VariableCoefficientStd. Errort-StatisticProb. C-1147.443396.1630-2.8963900.0093X0.1700520.01145114.849900.0000R-squared0.920675 Mean dependent var3080.390Adjusted R-squared0.916500 S.D. dependent var4368.710S.E. of regression1262.402 Akaike info criterion17.20981Sum squared resid30279518 Schwarz criterion17.30929Log likelihood-178.7030 F-statistic220.5196Durbin-Watson stat0.688670 Prob(F-statistic)0.0000002、自相关检验 （1）图示法由上述OLS计算，可直接得到残差RESID，运用GENR命令生成序列E，则在QUICK菜单中选GRAPH，在图形对话框中输入：E E(-1)，再点击SCATTER DIOGRAM。得结果如下，从图中可以看出残差et呈线性自回归，表明随机误