Eviews实验报告

1、江西农业大学经济贸易学院学生实验报告课程名称：计量经济学专业班级：经济1201班姓 名：学 号：指导教师：徐冬梅职 称：讲师实验日期：2014.12.11学生实验报告学生姓名学号组员：实验项目EVIEWS勺使用日必修口选修日演示性实验 R验证性实验口操作性实验口综合性实验实验地点管理模拟实验室实验仪器台号指导教师实验日期及节次一、实验目的及要求1、目的会使用EVIEWS寸计量经济模型进行分析2、内容及要求(1)对经典线形回归模型进行参数估计、参数的检验与区间估计, 对模型总体进行显著性检验；(2)异方差的检验及其处理；(3)自相关的检验及其处理；(4)多重共线性检验及其处理；二、仪器用具仪器名

2、称规格/型号数量备注计算机1无网络环境Eviews1三、实验方法与步骤(一)数据的输入、描述及其图形处理;（二）方程的估计；（三）参数的检验、违背经典假定的检验；（四）模型的处理与预测25000 20000 -:»*Y 15000 -） *10000 - 1 噂*5000 -I11116000800010000 12000 14000 16000X四、实验结果与数据处理实验一：中国城镇居民人均消费支由模型数据散点图：通过Eviews估计参数方程回归方程：Dependent Variable: YMethod: Least SquaresDate: 11/27/14 Time: 15:

3、02Sample: 1 31Included observations: 31VariableCoefficien Std. Error t-StatisticProb.1.3594770.04330231.395250.0000-57.90655377.7595-0.1532890.8792R-squared0.971419Mean dependent var11363.6915000001000000500000Adjusted R-squared0.970433S.D. dependent var3294.4690 6000800010000 12000 14000 16000XS.E.

4、 of regression566.4812Akaike info15.57911criterionSum squared resid9306127.Schwarz criterion15.67162Log likelihood-239.4761F-statistic985.6616Durbin-Watson stat1.294974Prob(F-statistic)0.000000得出估计方程为：Y = 1.35947661442*X - 57.9065479515异方差检验1、图示检验法图形呈现离散趋势，大致判断存在异方差性。2、Park检验Dependent Variable: LOG(

5、E2)Method: Least SquaresDate: 11/27/14 Time: 16:16Sample: 1 31Included observations: 31VariableCoefficienStd. Error t-StatisticProb.CLOG(X)19.82562-0.95640319.853590.9985912.204080-0.4339240.32630.6676R-squared0.006451Mean dependent var11.21371Adjusted R-squared-0.027809S.D.dependent var2.894595S.E.

6、 of regression2.934568Akaike info5.053338criterionSum squared resid249.7389Schwarz criterion5.145854Log likelihood-76.32674F-statistic0.188290Durbin-Watson stat2.456500Prob(F-statistic)0.667555看到图中LOG(E2冲P值为0.6676 > 0.05，所以不存在异方差性3、G-Q检验ei检验：Dependent Variable: XMethod: Least SquaresDate: 11/27/1

7、4 Time: 16:41Sample: 1 12Included observations: 12VariableCoefficientStd. Error t-StatisticProb.C4642.0282014.1832.3046710.0439Y0.2310460.2158241.0705300.3095R-squared0.102820Mean dependent var6796.390Adjusted R-squared0.013102S.D.dependent var293.2762S.E. of regression291.3486Akaike info14.33793cri

8、terionSum squared resid848840.2Schwarz criterion14.41875Log likelihood-84.02758F-statistic1.146034Durbin-Watson stat0.445146Prob(F-statistic)0.309538e2检验：Dependent Variable: X Method: Least SquaresDate: 11/27/14 Time: 16:42Sample: 20 31Included observations: 12VariableCoefficientStd. Error t-Statist

9、icProb.C583.4526593.43700.9831750.3487Y0.6977480.04019617.358700.0000R-squared0.967879Mean dependent var10586.89Adjusted R-squared0.964667S.D.dependent var2610.864S.E. of regression490.7655Akaike info15.38082criterionSum squared resid2408507.Schwarz criterion15.46164Log likelihood-90.28493F-statisti

10、c301.3245Durbin-Watson stat2.748144Prob(F-statistic)0.000000第一个图中的残差平方和为 848840.2第二个图中的残差平方和为 2408507所以F值为2408507/848840.2 = 2.8374 < 2.97,所以不存在异方差性4、White 检验White Heteroskedasticity Test:F-statistic2.240402Probability0.125152Obs*R-squared4.276524Probability0.117860Test Equation:Dependent Variabl

11、e: RESIDA2Method: Least SquaresDate: 11/27/14 Time: 16:50Sample: 1 31Included observations: 31VariableCoefficientStd. Error t-StatisticProb.C-2135113.1158576.-1.8428760.0760X503.7331242.20782.0797560.0468XA2-0.0236090.011650-2.0265900.0523R-squared0.137952Mean dependent var300197.6Adjusted R-squared

12、0.076378S.D.dependent var347663.4S.E. of regression334122.9Akaike info28.36817criterionSum squared resid3.13E+12Schwarz criterion28.50694Log likelihood-436.7067F-statistic2.240402Durbin-Watson stat1.871252Prob(F-statistic)0.125152P值为0.11786 > 0.05，所以不存在异方差性通过四种不同的检验得知除了图示检验法得出异方差的结论，其他的检验的结论都是不存在

13、异方差的。5、WLS(加权最小二乘法)修正Dependent Variable: YMethod: Least SquaresDate: 11/27/14 Time: 17:14Sample: 1 31Included observations: 31Weighting series: E3VariableCoefficientStd. Error t-StatisticProb.C-85.6942624.15675-3.5474250.0013X1.3622210.002307590.56150.0000Weighted StatisticsR-squared1.000000Mean dep

14、endent var13474.53Adjusted R-squared1.000000S.D.dependent var61353.74S.E. of regression27.93264Akaike info9.559810criterionSum squared resid22626.73Schwarz criterion9.652325Log likelihood-146.1770F-statistic348762.9Durbin-Watson stat2.061818Prob(F-statistic)0.000000UnweightedStatisticsR-squared0.971

15、413Mean dependent var11363.69Adjusted R-squared0.970427S.D.dependent var3294.469S.E. of regression566.5415Sum squared resid9308110.Durbin-Watson stat2.178992实验二：中国粮食生产函数1、回归方程Dependent Variable: LOG(Y)Method: Least SquaresDate: 12/11/14 Time: 15:06Sample: 1983 2007Included observations: 25VariableCo

16、efficien Std. Error t-StatisticProb.LOG(X1)0.3811450.0502427.5861820.0000LOG(X2)1.2222890.1351799.0420300.0000LOG(X3)-0.0811100.015304-5.3000240.0000LOG(X4)-0.0472290.044767-1.0549800.3047LOG(X5)C-0.101174-4.1731740.057687-1.7538531.923624-2.1694340.09560.0429R-squared0.981597Mean dependent var10.70

17、905Adjusted R-squared0.976753S.D.dependent var0.093396S.E. of regression0.014240Akaike info-5.459968criterionSum squared resid0.003853Schwarz criterion-5.167438Log likelihood74.24960F-statistic202.6826Durbin-Watson stat1.791427Prob(F-statistic)0.000000得出回归方程为:LOG(Y) = 0.381144581612*LOG(X1) + 1.2222

18、8859801*LOG(X2) - 0.0811098881534*LOG(X3) -0.04722870996*LOG(X4) - 0.101173736285*LOG(X5) - 4过检验结果可知 R2较大且接近于1,而且F=202.6826 闩。5（5,19） = 2.74 ,故认为粮食产量与上述变量之间总体线性关系显著。但是由于其中X、X5前的参数估计值未通过t检验，且符号的经济意义不合理，故认为解释变量之间存在多重共线。2、相关系数表LNX1LNX2LNX3LNX4LNX5LNX11.000000-0.5687440.4517000.9643570.4402

19、05LNX2-0.5687441.000000-0.214097-0.697625-0.073270LNX30.451700-0.2140971.0000000.3987800.411279LNX40.964357-0.6976250.3987801.0000000.279528LNX50.440205-0.0732700.4112790.2795281.000000由表可知LnX与LnX2之间存在高度的线性相关性3、简单的回归形式LnY 与 LnXDependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:15Sam

20、ple: 1983 2007Included observations: 25VariableCoefficientStd. Error t-StatisticProb.LNX10.2240050.0255158.7792930.0000C8.9020080.20603443.206570.0000R-squared0.770175Mean dependent var10.70905Adjusted R-squared0.760182S.D.dependent var0.093396S.E. of regression0.045737Akaike info-3.255189criterionS

21、um squared resid0.048114Schwarz criterion-3.157679Log likelihood42.68986F-statistic77.07599Durbin-Watson stat0.939435Prob(F-statistic)0.000000LnY 与 LnXDependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:16Sample: 1983 2007Included observations: 25VariableCoefficientStd. Error t-Stati

22、sticProb.LNX2-0.3834340.509669-0.7523210.4595C15.157485.9129712.5634290.0174R-squared0.024017Mean dependent var10.70905Adjusted R-squared-0.018417S.D.dependent var0.093396S.E. of regression0.094252Akaike info-1.809063criterionSum squared resid0.204321Schwarz criterion-1.711553Log likelihood24.61329F

23、-statistic0.565986Durbin-Watson stat0.335219Prob(F-statistic)0.459489LnY 与 LnX3Dependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:18Sample: 1983 2007Included observations: 25VariableCoefficientStd. Error t-StatisticProb.LNX30.1080670.0852711.2673350.2177C9.6197220.85974411.189050.00

24、00R-squared0.065274Mean dependent var10.70905Adjusted R-squared0.024634S.D.dependent var0.093396S.E. of regression0.092239Akaike info-1.852255criterionSum squared resid0.195684Schwarz criterion-1.754745Log likelihood25.15319F-statistic1.606139Durbin-Watson stat0.597749Prob(F-statistic)0.217717LnY 与

25、LnX4Dependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:18Sample: 1983 2007Included observations: 25VariableCoefficientStd. Error t-StatisticProb.LNX40.1669760.0282745.9056700.0000C8.9490900.29825530.004790.0000R-squared0.602605Mean dependent var10.70905Adjusted R-squared0.585327S.D.

26、dependent var0.093396S.E. of regression0.060143Akaike info-2.707578criterionSum squared resid0.083194Schwarz criterion-2.610068Log likelihood35.84472F-statistic34.87693Durbin-Watson stat0.625528 Prob(F-statistic)0.000005LnY 与 LnX5Dependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:19

27、Sample: 1983 2007Included observations: 25VariableCoefficientStd. Error t-StatisticProb.LNX50.4887310.2346062.0831990.0485C5.6007492.4522072.2839620.0319R-squared0.158733Mean dependent var10.70905Adjusted R-squared0.122156S.D.dependent var0.093396S.E. of regression0.087506Akaike info-1.957599criteri

28、onSum squared resid0.176118Schwarz criterion-1.860089Log likelihood26.46999F-statistic4.339718Durbin-Watson stat0.327932Prob(F-statistic)0.048538比较各个回归方程的R2可知Y与X的R2最大，即粮食生产受农业化肥施用量最大,与经验相符，因此选为初始的回归方程。且初始化回归方程为:LOG(Y) = 0.224004867873*LOG(X1) + 8.90200821784R2 = 0.770175D.W. = 0.9394354、逐步回归LnY 与 Ln

29、XDependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:28Sample: 1983 2007Included observations: 25VariableCoefficientStd. Error t-StatisticProb.LNX10.2240050.0255158.7792930.0000C8.9020080.20603443.206570.0000R-squared0.770175Mean dependent var10.70905Adjusted R-squared0.760182S.D.dep

30、endent var0.093396S.E. of regression0.045737Akaike info-3.255189criterionSum squared resid0.048114Schwarz criterion-3.157679Log likelihood42.68986F-statistic77.07599Durbin-Watson stat0.939435Prob(F-statistic)0.000000LnY与 LnX、Ln%Dependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:29Sa

31、mple: 1983 2007Included observations: 25VariableCoefficientStd. Error t-StatisticProb.LNX10.2978540.01548219.239290.0000LNX21.2586220.1500668.3871270.0000C-6.2956821.814941-3.4688090.0022R-squared0.945246Mean dependent var10.70905Adjusted R-squared0.940269S.D.dependent var0.093396S.E. of regression0

32、.022826Akaike info-4.609666Sum squared resid0.011463 Schwarz criterion-4.463401Log likelihood60.62083F-statistic189.9002Durbin-Watson stat1.595748Prob(F-statistic)0.000000由输出结果可知R2有所提高，且各解释变量前得参数均通过t检验，符号也合理D.W.检验也表明不存在一阶自相关。可以考虑再此模型上继续引入X3。LnY 与 LnX、Ln%、LnXDependent Variable: LNYMethod: Least Squar

33、esDate: 12/11/14 Time: 15:30Sample: 1983 2007Included observations: 25VariableCoefficien Std. Error t-StatisticProb.LNX10.3233850.01086129.775520.0000LNX21.2907290.09615313.423650.0000LNX3-0.0867540.015155-5.7244840.0000C-5.9996381.162078-5.1628520.0000R-squared0.978616Mean dependent var10.70905Adju

34、sted R-squared0.975561S.D.dependent var0.093396S.E. of regression0.014601Akaike info-5.469854criterionSum squared resid0.004477Schwarz criterion-5.274834Log likelihood72.37318F-statistic320.3438Durbin-Watson stat1.412883Prob(F-statistic)0.000000由输出结果可知R2再次提高且参数符号合理，变量通过t检验。但是D.W.=1.419（a=1.12、dU=1.6

35、6）落入无法判断的区域，且 X4的参数没有通过t检验。LM检验Breusch-Godfrey Serial Correlation LM Test:F-statistic1.241319Probability0.278428Obs*R-squared1.460972Probability0.226776Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 12/11/14 Time: 15:43VariableCoefficien Std. Error t-StatisticProb.LNX10.0024030.01

36、10120.2182250.8295LNX20.0069520.0958090.0725610.9429LNX3-0.0054780.015850-0.3455890.7333C-0.0447291.156156-0.0386880.9695RESID(-1)0.2574590.2310821.1141450.2784R-squared0.058439Mean dependent var1.07E-16Adjusted R-squared-0.129873S.D.dependent var0.013658S.E. of regression0.014517Akaike info-5.45007

37、0criterionSum squared resid0.004215Schwarz criterion-5.206295Log likelihood73.12588F-statistic0.310330Durbin-Watson stat1.794969Prob(F-statistic)0.867655LM检验显示不存在一阶自相关，继续引入 XLnY 与 LnX、Ln%、LnX、LnX4Dependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:32Sample: 1983 2007Included observat

38、ions: 25VariableCoefficientStd. Error t-StatisticProb.LNX10.3220610.0391618.2239570.0000LNX21.2940010.1353689.5591170.0000LNX3-0.0866650.015730-5.5095090.0000LNX40.0013030.0369720.0352510.9722C-6.0415541.682783-3.5902150.0018R-squared0.978617Mean dependent var10.70905Adjusted R-squared0.974341S.D.de

39、pendent var0.093396S.E. of regression0.014961Akaike info-5.389916Sum squared resid0.004476Schwarz criterionLog likelihood72.37395F-statisticDurbin-Watson stat1.413284Prob(F-statistic)criterion-5.146141228.83160.0000002 .由输出结果可知R有所下降，且X4的参数未能通过t检验。去掉X4引入X5LnY 与 LnX1、LnX2、LnK、LnX5Dependent Variable: LNYMethod: Least SquaresDate: 12/11/14 Time: 15:33Sample: 1983 2007Included observations: 2