eviews面板数据实例分析(包会)

1、1.已知19962002年中国东北、华北、华东15个省级地区的居民家庭人均消费（cp，不变价格）和人均收入（0,不变价格）居民，利用数据（1）建立面板数据（paneldata）工作文件；（2）定义序列名并输入数据；（3）估计选择面板模型；（4）面板单位根检验。年人均消费（consume）和人均收入（income）数据以及消费者价格指数（p）分别见表9.1，9.2和9.3。表9.119962002年中国东北、华北、华东15个省级地区的居民家庭人均消费（元）数据人均消费1996199719981999200020012002CONSUMEAH3607.433693.553777.413901.81

2、4232.984517.654736.52CONSUMEBJ5729.526531.816970.837498.488493.498922.7210284.6CONSUMEFJ4248.474935.955181.455266.695638.746015.116631.68CONSUMEHB3424.354003.713834.434026.34348.474479.755069.28CONSUMEHLJ3110.923213.423303.153481.743824.444192.364462.08CONSUMEJL3037.323408.033449.743661.684020.87433

3、7.224973.88CONSUMEJS4057.54533.574889.435010.915323.185532.746042.6CONSUMEJX2942.113199.613266.813482.333623.563894.514549.32CONSUMELN3493.023719.913890.743989.934356.064654.425342.64CONSUMENMG2767.843032.33105.743468.993927.754195.624859.88CONSUMESD3770.994040.634143.964515.0550225252.415596.32CONS

4、UMESH6763.126819.946866.418247.698868.199336.110464CONSUMESX3035.593228.713267.73492.983941.874123.014710.96CONSUMETJ4679.615204.155471.015851.536121.046987.227191.96CONSUMEZJ5764.276170.146217.936521.547020.227952.398713.08表9.219962002年中国东北、华北、华东15个省级地区的居民家庭人均收入（元）数据人均收入1996199719981999200020012002

5、INCOMEAH4512.774599.274770.475064.65293.555668.86032.4INCOMEBJ7332.017813.168471.989182.7610349.6911577.7812463.92INCOMEFJ5172.936143.646485.636859.817432.268313.089189.36INCOMEHB4442.814958.675084.645365.035661.165984.826679.68INCOMEHLJ3768.314090.724268.54595.144912.885425.876100.56INCOMEJL3805.53

6、4190.584206.644480.0148105340.466260.16INCOMEJS5185.795765.26017.856538.26800.237375.18177.64INCOMEJX3780.24071.324251.424720.585103.585506.026335.64INCOMELN4207.234518.14617.244898.615357.795797.016524.52INCOMENMG3431.813944.674353.024770.535129.055535.896051INCOMESD4890.285190.795380.085808.966489

7、.977101.087614.36INCOMESH8178.488438.898773.110931.6411718.0112883.4613249.8INCOMESX3702.693989.924098.734342.614724.115391.056234.36INCOMETJ5967.716608.397110.547649.838140.58958.79337.56INCOMEZJ6955.797358.727836.768427.959279.1610464.6711715.6表9.319962002年中国东北、华北、华东15个省级地区的消费者物价指数物价指数199619971998

8、1999200020012002PAH109.9101.310097.8100.7100.599PBJ111.6105.3102.4100.6103.5103.198.2PFJ105.9101.799.799.1102.198.799.5PHB107.1103.598.498.199.7100.599PHLJ107.1104.4100.496.898.3100.899.3PJL107.2103.799.29898.6101.399.5PJS109.3101.799.498.7100.1100.899.2PJX108.410210198.6100.399.5100.1PLN107.9103.19

9、9.398.699.910098.9PNMG107.6104.599.399.8101.3100.6100.2PSD109.6102.899.499.3100.2101.899.3PSH109.2102.8100101.5102.5100100.5PSX107.9103.198.699.6103.999.898.4PTJ109103.199.598.999.6101.299.6PZJ107.9102.899.798.810199.899.1（1）建立面板数据工作文件首先建立工作文件。打开工作文件后，过程如下：建立面板数据库。在窗口中输入15个不同省级地区的标识。2）定义序列名并输入数据（3）估

10、计、选择面板模型打开一个pool窗口，先输入变量后缀（所要使用的变量）。点击Estimate,打开估计窗口。Pool:POOL1lorkfile:UNTITLED:Untitled回冈ZJA.混合模型的估计方法左边的Common表示相同系数，即表示不同个体有相同的斜率。得到如下输出结果：DependentVariable:CP?Method:PooledLeastSquaresDate:07/02/08Time:13:13Sample:19962002Includedobservations:7Cross-sectionsincluded:15Totalpool(balanced)observ

11、ations:105VariableCoefficientStd.Errort-StatisticProb.C129.631363.692592.0352650.0444IP?0.7587260.00952279.681890.0000R-squared0.984036Meandependentvar4917.608AdjustedR-squared0.983881S.D.dependentvar1704.704S.E.ofregression216.4270Akaikeinfocriterion13.61125Sumsquaredresid4824588.Schwarzcriterion13

12、.66180Loglikelihood-712.5905F-statistic6349.204Durbin-Watsonstat0.784107Prob(F-statistic)0.000000相应的表达式是：CP=129.63+0.76IPitit(2.0)(79.7)R2=0.9&SSE=4824588r上式表示15个省级地区的城镇人均指出平均占收入的76%。B个体固定效应回归模型的估计方法将截距项选择区选Fixedeffects(固定效应)得到如下输出结果：DependentVariable:CP?Method:PooledLeastSquaresDate:07/02/08Time:13

13、:36Sample:19962002Includedobservations:7Cross-sectionsincluded:15Totalpool(balancEd)observations:105VariableCoEffiuiEntStd.Errort-StatisticProb.C515.614281.596656.319061.0000IP?0.6975610.01269254.96029.0000FixedEffects(Cross)AH-C-36.30583BJ-C537.5660FJ-C-47.64515HB-C-154.2367HU-C-169.7015JL-C24.5042

14、7JS-C-35.19587JX-C-319.6960LN-C106.4273NMG-C-209.5484SD-C-134.1145SH-C266.9859SX-C-74.88901TJ-C47.22940ZJ-C198.6202EffectsSpecificationCross-sectionfixed(dummyvariables)R-squarEd0.992488MeandepEndEntvar4917.608AdjustedR-squared0.991222S.D.dependentvar1704.704S.E.ofegression159.7184Akaikeinfocriterio

15、n13.12414Sumsquaredresid2270386.Schwarzcriterion13.52855Loglihood-673.0173F-statistic783.8902Durbin-Watsonstat1.609517Prob(F-statistic)0.000000相应的表达式为：CP=515.6+0.70IP-36.3D+537.6D+.+198.6Ditit1215(6.3)(55)R2二0.99,SSE=2270386r其中虚拟变量D,D，,D的定义是:12151,如果属于第个个体,i=1,2,.,15D=i0,其他15个省级地区的城镇人均指出平均占收入70%。从上面

16、的结果可以看出北京市居民的自发性消费明显高于其他地区。接下来用F统计量检验是应该建立混合回归模型，还是个体固定效应回归模型。H:a“。模型中不同个体的截距相同(真实模型为混合回归模型)。0iH:模型中不同个体的截距项a不同(真实模型为个体固定效应回归模型)。1iF统计量定义为：(SSE-SSE)/(NT-k-1)-(NT-N-k)_(SSE-SSE)/(N-1)SSE/(NT-N-k)SSE/(NT-N-k)uu其中SSE表示约束模型，即混合估计模型的残差平方和，SSE表示非约束模型，即ru个体固定效应回归模型的残差平方和。非约束模型比约束模型多了N-1个被估参数。所以本例中：验总當：1)饗F

17、o.o514,89)-1-8所以推翻原假设，建立个体固定效应回归模型更合理。C时点固定效应回归模型的估计方法将时间选择为固定效应。得到如下输出结果：DependentVariable:CP?Method:PooledLeastSquaresDate:07/02/08Time:14:44Sample:19962002Includedobservations:7Cross-sectionsincluded:15Totalpool(balanced)observations:105VariableCoefficientStd.Errort-StatisticProb.C2.57723468.8054

18、80.0374570.9702IP?0.7788600.01043874.619110.0000FixedEffects(Period)1996-C105.92851997-C134.05581998-C54.848281999-C-37.074042000-C-7.1183592001-C-156.74742002-C-93.89280EffectsSpecificationPeriodfixed(dummyvariables)R-squared0.986669Meandependentvar4917.608AdjustedR-squared0.985707S.D.dependentvar1

19、704.704S.E.ofregression203.8001Akaikeinfocriterion13.54529Sumsquaredresid4028843.Schwarzcriterion13.74749Loglikelihood-703.1276F-statistic1025.643Durbin-Watsonstat0.785376Prob(F-statistic)0.000000相应的表达式为：CP=2.6+0.78IP+105.9D+134.1D+.-93.9Ditit127(76.6)R2二0.987,SSE=4028843其中虚拟变量D,D，,D的定义是：1271,如果属于第t

20、个截面，t=1996,.,2002D=t|0,其他D个体随机效应回归模型估计截距项选择Randomeffects（个体随机效应）得到如下部分输出结果：DependentVariable:CP?Method:PooledEGLS(Cross-sectionrandomeffects)Date:07/02/00Time:15:06Sample:19962002Includedobservations:7Cross-sectionsincluded:15Totalpool(balanced)observations:105SwamyandAroraestimatorofcomponentvarian

21、cesVariableCoefficientStd.Errort-StatisticProb.C345.179575.472174.5735990.0000IP?0.7245690.01057260.530140.0000RandomEffects(Cross)AH-C-2.553433BJ-C367.0439FJ-C-54.24006HB-C-104.8367HM-C-101.7680JL-C54.90671JS-C-32.27860JX-C-223.9519LN-C112.1152NMG-C-133.1377SD-C-100.8713SH-C126.1820SX-C-22.79189TJ-

22、C10.08794ZJ-C106.0939相应的表达式是：CP=345.2+0.72IP-2.6D+367.0D+.+106.1Ditit1215(68.5)R2二0.9&SSE二2979246其中虚拟变量D,D，,D的定义是：1215八1,如果属于第i个个体,i=l,2,.,15D=OK得到如下检验结果：Grein口unitrwttmst:Summ日问Date:07/02/08Time:15:40Sample:19962002Series:CPAH,CPBJ,CPFJ,CPHB,CPHU,CPJL,CPUS,CPJX,CPLN,CPNMG,CPSD,CPSH,CPSX,CPTJ,CPUExo

23、genousvariables:IndividualeffectsAutomaticselectionofmaximumlagsAutomaticselectionoflagsbasedonSIC:0to1Newey-WestbandwidthselectionusingBartlettkernelMethodStatisticProb.*CrosssectionsObsNull:Unitroot(assumescommonunitrootprocess)Levin,Lin&Chut*9.697781.00001584Null:Unitroot(assumesindividualunitroo

24、tprocess)Im,PesaranandShinW-stat6.463801.00001584ADF-FisherChi-square3.049971.00001584PP-FisherChi-square5.243351.00001590Null:Nounitroot(assumescommonunitrootprocess)HadriZ-stat7.503830.000015105*ProbabilitiesforFishertestsarecomputedusinganasympoticChi-squaredistribution.Allothertestsassumeasympto

25、ticnormality从上面的检验结果可以看出来，6种检验方法的结论都认为15个cp序列存在单位根。选择IPS检验方法进行单位根检验。检验结果如下:NullHypothesis:Unitroot(individualunitrootprocess)Date:07/02/08Time:15:44Sample:19962002Series:CPAH,CPBJ,CPFJ,CPHB,CPHM,CPJL,CPUS,CPJX,CPLN,CPNMG,CPSD,CPSH,CPSX,CPTJ,CPZJExogenousvariables:IndividualeffectsAutomaticselectiono

26、fmaximumlagsAutomaticselectionoflagsbasedonSIC:0to1Totalnumberofobservations:84Cross-sectionsincluded:15MethodStatisticProb.*Im,PesaranandShinW-stat6.463801.0000*ProbabilitiesarecomputedassumingasympoticnormalityIntermediateADFtestresultsSeriest-StatProb.Eft)E(Var)LaciMaxLaqObsCPAH5.55420.9999-1.558

27、2.648115CPBJ0.56130.9695-1.5472.332016CPFJ-0.69697711-1.5472.332016CPHB-0.53040.8149-1.5472.332016CPHM2.65880.9991-1.5472.332016CPJL0.63240.9732-1.5472.332016CPUS-1.35320.5305-1.5472.332016CPJX0.53560.9601-1.5472.332016CPLN1.12760.9893-1.5472.332016CPNMG3.07650.9992-1.5582.648115CPSD-0.33890.8450-1.

28、5582.648115CPSH0.78710.9754-1.5582.648115CPSX2.11120.9980-1.5472.332016CPTJ-0.15460.8827-1.5582.648115CPZJ2.00920.9966-1.5582.648115Average1.0653-1.5522.459从上面的结果可以看出，cp面板存在单位根，同时每个个体都存在单位根。2收集中国20002005年各地区城镇居民人均可支配收入X和消费指出Y统计数据如表9.4。数据是6年的，每一年都有32组数据，共192组观测值。人均可支配收入和消费支出数据（单位：元）2000可支200120022003

29、20042005可支配收消费可支配收消费可支配收消费可支配收消费可支配收消费地配收消费区入支出入支出入支出入支出入支出入支出XYXYXYXYXYXY全6279.949986859.553097702.860298472.265109421.6718210493.7942国8.008.010.880.941.1003.88北10349.849311577.892212463.102813882.111215637.122017652.1324京69.4978.72924.60623.84840.40954.20天8140.561218958.769879337.5719110312.786711

30、467.880212638.9653津0.040.226.9691.5316.4455.26河5661.143485984.844796679.650697239.054397951.358199107.06699北6.472.758.286.771.189.67山4724.139415391.041236234.347107005.051057902.856548913.96342西内1.875.016.963.386.151.63内蒙5129.039275535.841956051.048597012.954198122.962199136.76928古5.759.620.880.149.

31、269.60辽5357.743565797.046546524.553427240.560778007.565439107.57369宁9.061.422.648.926.285.27吉4810.040205340.443376260.149737005.154927840.660688690.66794林0.876.226.887.101.992.71黑八、龙4912.838245425.841926100.544626678.950157470.755678272.56178江8.447.366.080.191.531.01上11718.886812883.933613249.104614

32、867.110416682.126318645.1377海01.1946.10804.00490.34821.03033.41江6800.253237375.155328177.660429262.4670810481.733212318.8621苏3.180.744.606.5893.2657.82浙9279.1702010464.795211715.871313179.971214546.106316293.1225江6.2267.3960.0853.89386.14773.74安5293.542325668.845176032.447366778.050647511.457118470.

33、66367徽5.980.650.523.343.338.67福7432.256388313.060159189.366319999.5735611175.816112321.8794建6.748.116.684.2637.1531.41江5103.536235506.038946335.645496901.449147559.653378619.66109西8.562.514.322.554.846.39山6489.950227101.052527614.355968399.960699437.8667310744.7457东7.008.416.321.350.7579.31河4766.238

34、305267.441106245.445046926.149417704.952948667.96038南6.712.170.682.600.197.02湖5524.546445855.948046788.556087321.959638022.763988785.96736北4.508.792.928.255.524.56湖6218.752186780.555466958.555747674.260828617.468849523.97504南3.796.226.720.628.617.99广9761.5801610415.809911137.898812380.963613627.1069

35、14769.1180东7.9119.6320.4843.27654.79949.87广5834.448526665.752247315.354137785.057638689.964459286.77032西3.313.732.444.509.730.80海5358.340825838.843676822.754597259.255027735.758028123.95928南2.564.852.645.438.404.79重6275.955696721.058737238.063608093.671189220.9797310243.8623庆8.849.694.247.066.0546.2

36、9四5894.248556360.451766610.854137041.857597709.863718385.96891川7.787.170.087.217.146.27贵5122.242785451.942735944.045986569.249487322.054948151.16159州1.281.908.283.985.453.29云6324.651856797.752527240.558277643.560238870.868379265.96996南4.311.606.927.568.010.90西7426.355547869.159948079.169528765.48045

37、9106.083389431.18617藏2.426.392.445.347.218.11陕5124.242765483.746376330.853786806.356667492.462338272.06656西4.673.744.045.547.072.46甘4916.241265382.944206151.450646657.252987376.759378086.86529肃5.471.314.244.914.302.20青5169.941855853.746986170.550426745.354007319.657588057.86245海6.732.592.522.247.955

38、.26宁4912.442005544.145956067.451046530.453307217.858218093.66404夏0.507.404.928.347.384.31新5644.844226395.049316899.656367173.555407503.457737990.16207疆6.934.404.404.612.625.52首先建立工作文件，打开工作文件后，过程如下：建立面板数据库，并命名为XY。输入不同省市（包括全国）的标识，如下：点击sheet键，定义变量X和Y。点击Edt+/-后，在数据窗口键入数据即可。对模型进行估计，建立个体固定效应回归模型，过程如下：得到如下

39、输出结果：DependentVariable:Y?Method:PooledLeastSquaresDate:07/08/08Time:19:12Sample:20002005Includedobservations:6Cross-sectionsincluded:32Totalpool(balanced)observations:192VariableCoefficientStd.Errort-StatisticProb.c634.3534X?0.699629FixedEffects(Cross)00QG-C-45.914151BJ-C566.338802TJ-C40.1904603HB-C-295.