计量经济学报告

1、实 验 报 告课程名称： 计量经济学 任课教师： 实验日期 班级： 姓名： 学号： 实验项目名称: 专门问题模型一、实验目的及要求 1.使学生了解常用计量经济学软件的基本应用，熟悉在信息化条件下的经济学中数据处理的一般流程和方法，增强对经济数据计量化的感性认识。2.使学生熟练开发工具和平台的使用，增强实践动手能力，提高信息技术的应用能力。3.通过统计检验加深对放宽基本假定的模型的了解。二、实验环境1系统软件：Eviews 3.02工具：Eviews 3.0三、实验内容与步骤（1）虚拟变量模型1、统计第一产业当月固定资产投资额，并作出折线图，分析折线图找出转折点，与题目所给的进行比较分析。、1）

2、对 Y DD C 回归：VariableCoefficientStd. Errort-StatisticProb.DD-26.9109512.98339-2.0727220.0475C79.110957.11129411.124690.0000R-squared0.133024Mean dependent var71.03767Adjusted R-squared0.102061S.D. dependent var34.39021S.E. of regression32.58804Akaike info criterion9.870108Sum squared resid29735.45Sch

3、warz criterion9.963521Log likelihood-146.0516F-statistic4.296177Durbin-Watson stat1.774765Prob(F-statistic)0.047518由 p=0.0475，通过显著性检验，接受此点是转折点的检验。但R 2 = 0.102061，统计结果的解释力度不大。加变量时间T，2） 对 Y T DD C 进行回归。结果如下：Dependent Variable: Y Method: Least SquaresDate: 11/30/13Sample: 1 30Time: 20:14Included observ

4、ations: 30VariableCoefficientStd. Errort-StatisticProb.T2.6340961.0343612.5465920.0169DD12.6004919.536730.6449640.5244C26.4290221.685201.2187590.2335R-squared0.300933Mean dependent var71.03767Adjusted R-squared0.249151S.D. dependent var34.39021S.E. of regression29.79966Akaike info criterion9.721510S

5、um squared resid23976.53Schwarz criterion9.861630Log likelihood-142.8227F-statistic5.811461Durbin-Watson stat2.153268Prob(F-statistic)0.007962加入时间变量 T 后，虽然整体通过检验， R2 =0.249 也有增大，但是 T 和 DD 两变量都没有通过检验。 VariableCoefficientStd. Errort-StatisticProb.DD*T2.9008452.4592091.1795850.2485T2.6367000.7650243.44

6、65580.0019C25.8175515.310591.6862540.10333）乘法方式引入虚拟变量，回归结果如下： Y DD*T T CR-squared0.324951Mean dependent var71.03767Adjusted R-squared0.274947S.D. dependent var34.39021S.E. of regression29.28327Akaike info criterion9.686549Sum squared resid23152.77Schwarz criterion9.826669Log likelihood-142.2982F-sta

7、tistic6.498549Durbin-Watson stat2.242692Prob(F-statistic)0.004966从回归结果来看，整体通过检验，但 DD*T 未能通过检验， R2 =0.2749，比加法引入时得解释力度要大。 4）乘法加法方式引入，Y DD*T DD T C 回归结果如下： VariableCoefficientStd. Errort-StatisticProb.DD*T4.1849293.9866771.0497280.3035DD-12.8620231.12259-0.4132700.6828T2.3315711.0718842.1752090.0389 C

8、32.4795222.398691.4500630.1590R-squared0.329356Mean dependent var71.03767Adjusted R-squared0.251974S.D. dependent var34.39021S.E. of regression29.74357Akaike info criterion9.746669Sum squared resid23001.67Schwarz criterion9.933495Log likelihood-142.2000F-statistic4.256243Durbin-Watson stat2.255636Pr

9、ob(F-statistic)0.014245由回归结果看来，乘法加法引入，整体未能通过检验。因此，判断得出加法引入变量最合适。、对加法引入虚拟变量的回归结果进行序列相关性检验：Breusch-Godfrey Serial Correlation LM Test:F-statistic0.073869Probability 0.928987R-squared0.005650Mean dependent var1.89E-15Adjusted R-squared-0.109083S.D. dependent var32.02125S.E. of regression33.72253Akaike

10、info criterion9.997775Sum squared resid29567.44Schwarz criterion10.18460Log likelihood-145.9666F-statistic0.049246Durbin-Watson stat1.899406Prob(F-statistic)0.985211 Obs*R-squared0.169504Probability0.918740Test Equation:Dependent Variable: RESIDMethod: Least Squares Date: 12/03/13Time: 20:01 Variabl

11、e CoefficientStd. Errort-StatisticProb.DD 0.11501913.450070.0085520.9932C 0.1226397.3659110.0166500.9868RESID(-1) 0.0599430.2008840.2983970.7678RESID(-2) 0.0464190.2019540.2298500.8200由检验结果可以看出，一阶 p=0.7678，接受原假设，因此加法引入虚拟变量不存在序列相关性。2、统计第二产业当月固定资产投资额，并作出折线图，从折线图看出，整体呈现上升趋势。、1） 作 Y DD C 回归分析： VariableC

12、oefficientStd. Errort-StatisticProb.DD*T2.53185731.393100.0806500.9362T70.6504613.164215.3668580.0000C1354.557305.50524.4338250.0001Dependent Variable: Y Method: Least SquaresDate: 11/30/13Time: 20:26 Sample: 1 30Included observations: 30VariableCoefficientStd. Errort-StatisticProb.DD-947.1378264.58

13、92-3.5796530.0013C2705.617144.921518.669530.0000R-squared0.313960Mean dependent var2421.475Adjusted R-squared0.289458S.D. dependent var787.8574S.E. of regression664.1137Akaike info criterion15.89912Sum squared resid12349317Schwarz criterion15.99254Log likelihood-236.4869F-statistic12.81392Durbin-Wat

14、son stat1.883507Prob(F-statistic)0.001281从上述回归结果看，整体结果较显著地通过检验， R2 =0.289458，解释力度较小，受时间变量的影响，加入时间 T，再进行回归。 VariableCoefficientStd. Errort-StatisticProb.DD90.73748406.71830.2230970.8249T73.8368020.146323.6650260.0009C1270.879502.17852.5307310.01672）加入时间 T，加法引入虚拟变量回归分析：R-squared0.573070Mean dependent

15、var2597.716Adjusted R-squared0.545526S.D. dependent var922.0611S.E. of regression621.6047Akaike info criterion15.78658Sum squared resid11978163Schwarz criterion15.92126Log likelihood-265.3719F-statistic20.80570Durbin-Watson stat2.296929Prob(F-statistic)0.000002由结果得出，整体通过检验，但 DD 未能通过检验， R2 =0.545526，

16、有增大。 3）乘法引入虚拟变量，回归如下：R-squared0.572474Mean dependent var2597.716Adjusted R-squared0.544892S.D. dependent var922.0611S.E. of regression622.0382Akaike info criterion15.78798Sum squared resid11994878Schwarz criterion15.92266Log likelihood-265.3956F-statistic20.75511Durbin-Watson stat2.293487Prob(F-stat

17、istic)0.000002从结果看来，整体通过检验，DD*T 仍为能通过检验，且 R2 =0.544892，较上述结果下降。 4）加法乘法同时引入虚拟变量，回归如下： VariableCoefficientStd. Errort-StatisticProb.DD*T-8.00523052.05960-0.1537700.8788DD172.7911674.93690.2560110.7997T75.3672122.762403.3110400.0024C1234.149563.41732.1904700.0364R-squared0.573406Mean dependent var2597.

18、716Adjusted R-squared0.530747S.D. dependent var922.0611S.E. of regression631.6310Akaike info criterion15.84462Sum squared resid11968730Schwarz criterion16.02419Log likelihood-265.3585F-statistic13.44149Durbin-Watson stat2.298528Prob(F-statistic)0.000010整体通过检验，但变量 DD*T，DD 均没有通过检验， R2 =0.530747，解释力度下降

19、，因此得出，加法引入虚拟变量最合适.、对加法引入虚拟变量的回归结果进行序列相关性检验：Breusch-Godfrey Serial Correlation LM Test:F-statistic0.366609Probability 0.696142 Obs*R-squared0.811156Probability0.666591Test Equation:Dependent Variable: RESIDMethod: Least Squares Date: 12/03/13Time: 19:41 Variable CoefficientStd. Errort-StatisticProb.D

20、D 5.635720264.00810.0213470.9831C 2.642147163.14120.0161950.9872RESID(-1) 0.1272630.1854160.6863650.4978RESID(-2) 0.0771250.1856150.4155100.6807R-squared0.023858Mean dependent var2.88E-13Adjusted R-squared-0.073757S.D. dependent var721.2856S.E. of regression747.4122Akaike info criterion16.18124Sum s

21、quared resid16758752Schwarz criterion16.36081Log likelihood-271.0811F-statistic0.244406Durbin-Watson stat1.965824Prob(F-statistic)0.864638由结果看，不存在序列相关。3、统计第三产业当月固定资产投资额，并作出折线图， VariableCoefficientStd. Errort-StatisticProb.DD-1332.413396.3316-3.3618660.0020C4117.958245.070416.803160.00001） 作 Y DD C 回

22、归分析，结果如下：R-squared0.261007Mean dependent var3608.506Adjusted R-squared0.237913S.D. dependent var1286.466S.E. of regression1123.054Akaike info criterion16.94251Sum squared resid40359988Schwarz criterion17.03230Log likelihood-286.0227F-statistic11.30214Durbin-Watson stat1.936929Prob(F-statistic)0.0020

23、18从结果看，整体通过检验，且 DD 也通过检验， R2 =0.237913，解释力度较小，加入时间变量 T，再回归。 VariableCoefficientStd. Errort-StatisticProb.DD4.197056690.00220.0060830.9952T78.6241534.178462.3004000.0283C2230.979851.95152.6186690.01352）Y DD T CR-squared0.368762Mean dependent var3608.506Adjusted R-squared0.328037S.D. dependent var1286

24、.466S.E. of regression1054.559Akaike info criterion16.84373Sum squared resid34474955Schwarz criterion16.97841Log likelihood-283.3434F-statistic9.054913Durbin-Watson stat2.221291Prob(F-statistic)0.000800从结果看,整体通过检验,但 DD 没有通过检验,R2 =0.328037,有所增大。 VariableCoefficientStd. Errort-StatisticProb.DD*T13.766

25、2853.164200.2589390.7974T81.7029322.293593.6648630.0009C2141.860517.37294.1398750.00022） 乘法引入虚拟变量，回归结果如下：R-squared0.370123Mean dependent var3608.506Adjusted R-squared0.329486S.D. dependent var1286.466S.E. of regression1053.421Akaike info criterion16.84157Sum squared resid34400592Schwarz criterion16.

26、97625Log likelihood-283.3067F-statistic9.107993Durbin-Watson stat2.228089Prob(F-statistic)0.000773整体通过检验，但 DD*T 没有通过检验， R2 =0.329486，增大。 3） 乘法加法引入虚拟变量，回归如下： VariableCoefficientStd. Errort-StatisticProb.DD*T36.0335188.109230.4089640.6855DD-365.14641142.309-0.3196560.7514T71.7353938.524641.8620650.072

27、4 C2396.309953.56602.5129970.0176R-squared0.372261Mean dependent var3608.506Adjusted R-squared0.309488S.D. dependent var1286.466S.E. of regression1069.015Akaike info criterion16.89699Sum squared resid34283821Schwarz criterion17.07657Log likelihood-283.2489F-statistic5.930199Durbin-Watson stat2.23556

28、3Prob(F-statistic)0.002658整体通过检验，但 DD，DD*T 没有通过检验，且 R2 =0.309488 减小。因此，加法引入虚拟变量最合适。 对加法引入虚拟变量进行序列相关性检验：Breusch-Godfrey Serial Correlation LM Test:F-statistic0.379369Probability 0.687529R-squared0.024667Mean dependent var3.34E-13Adjusted R-squared-0.072866S.D. dependent var1105.907S.E. of regression1

29、145.490Akaike info criterion17.03518Sum squared resid39364413Schwarz criterion17.21476Log likelihood-285.5981F-statistic0.252912Durbin-Watson stat1.946608Prob(F-statistic)0.858630 Obs*R-squared0.838690Probability0.657477Test Equation:Dependent Variable: RESIDMethod: Least Squares Date: 12/03/13Time:

30、 19:55 Variable CoefficientStd. Errort-StatisticProb.DD-8.502311404.3674-0.0210260.9834C-4.535801250.1269-0.0181340.9857RESID(-1) 0.0122780.1838970.0667640.9472RESID(-2)-0.1603400.184690-0.8681560.3922接受原假设，不存在相关性。（2）滞后变量模型统计某地区 1970-1991 年固定资产投资（y）与销售额（x）的数据，建立分布滞后模型来考察两者的关系。阿尔蒙变换 Lny pdl（lnx，2,2）c

31、Dependent Variable: LNY Method: Least SquaresDate: 11/30/13Time: 20:57 Sample(adjusted): 1972 1991Included observations: 20 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb.C-1.5278780.190656-8.0137970.0000PDL010.9401230.3428602.7420000.0145PDL02-0.5584030.174528-3.1995070.0056P

32、DL03-0.7942860.517184-1.5357890.1441R-squared0.993491Mean dependent var4.612088Adjusted R-squared0.992270S.D. dependent var0.522698S.E. of regression0.045955Akaike info criterion-3.145471Sum squared resid0.033789Schwarz criterion-2.946324Log likelihood35.45471F-statistic814.0290Durbin-Watson stat1.2

33、09248Prob(F-statistic)0.000000 Lag Distribution i CoefficienStd. ErrorT-Statisticof LNXt.*|00.704240.262272.68515.*|10.940120.342862.74200*.|2-0.412570.23087-1.78697Sum of1.231800.0347835.4213 LagsPdl03 没有通过检验。 Lny pdl（lnx，9,2）cDependent Variable: LOG(Y) Method: Least SquaresDate: 12/03/13Time: 20:1

34、3 Sample(adjusted): 1979 1991 Included observations: 13 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb.C-4.3322861.503392-2.8816730.0181PDL01-0.0480960.070412-0.6830630.5118PDL02-0.1652520.037429-4.4150380.0017 PDL030.0356600.0095373.7390970.0046R-squared0.961115Mean dependen

35、t var4.949667Adjusted R-squared0.948154S.D. dependent var0.209765S.E. of regression0.047763Akaike info criterion-2.997475Sum squared resid0.020532Schwarz criterion-2.823645Log likelihood23.48359F-statistic74.15129 Durbin-Watson stat1.732188Prob(F-statistic)0.000001LagiCoefficienStd. ErrorT-Statistic

36、 Distribution of LOG(X)t.*|01.183480.243114.86798.*|10.768600.159384.82252.*|20.425050.100874.21381. *|30.152820.073012.09300*.|4-0.048100.07041-0.68306* .|5-0.177690.07299-2.43457* .|6-0.235960.06995-3.37349* .|7-0.222910.06551-3.40271* .|8-0.138540.07965-1.73929*|90.017150.126710.13536Sum of1.7239

37、10.265196.50055LagsLny pdl（lnx，10,2）cDependent Variable: LOG(Y) Method: Least SquaresDate: 12/03/13Time: 20:14 Sample(adjusted): 1980 1991 Included observations: 12 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb.C-4.5837322.701115-1.6969780.1281PDL01-0.1402330.072980-1.921539

38、0.0909PDL02-0.1081810.043423-2.4913120.0374 PDL030.0301030.0079953.7653470.0055R-squared0.938713Mean dependent var4.982535Adjusted R-squared0.915730S.D. dependent var0.180776S.E. of regression0.052478Akaike info criterion-2.795640Sum squared resid0.022032Schwarz criterion-2.634005Log likelihood20.77

39、384F-statistic40.84431 Durbin-Watson stat1.685437Prob(F-statistic)0.000034LagiCoefficienStd. ErrorT-Statistic Distribution of LOG(X)t.*|01.153240.331213.48191.*|10.774130.241813.20143.*|20.455230.169942.67875. *|30.196540.117041.67918*|4-0.001950.08506-0.02293* .|5-0.140230.07298-1.92154* .|6-0.2183

40、10.07313-2.98517* .|7-0.236180.07880-2.99726* .|8-0.193850.09139-2.12120*.|9-0.091310.11726-0.77868.*|10.071430.160990.443710Sum of Lags1.768740.476463.71225各参数都通过了检验，且 R2 =0.915解释力度较大。Lny pdl（lnx，11,2）c Dependent Variable: LOG(Y) Method: Least SquaresDate: 12/03/13Time: 20:15 Sample(adjusted): 1981 1991 Included observations: 11 after adjusting