固定效应模型及估计原理说明

#/29word范文据（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1,2和3。表11996—2002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19961997199819992CONSUMEAH3607.433693.553777.413901.814232.984517.654736.52CONSUMEBJ5729.526531.816970.837498.488493.498922.7210284.6CONSUMEFJ4248.474935.955181.455266.695638.746015.116631.68CONSUMEHB3424.354003.713834.434026.34348.474479.755069.28CONSUMEHLJ3110.923213.423303.153481.743824.444192.364462.08CONSUMEJL3037.323408.033449.743661.684020.874337.224973.88CONSUMEJS4057.54533.574889.435010.915323.185532.746042.6CONSUMEJX2942.113199.613266.813482.333623.563894.514549.32CONSUMELN3493.023719.913890.743989.934356.064654.425342.64CONSUMENMG2767.843032.33105.743468.993927.754195.624859.88CONSUMESD3770.994040.634143.964515.0550225252.415596.32CONSUMESH6763.126819.946866.418247.698868.199336.110464CONSUMESX3035.593228.713267.73492.983941.874123.014710.96CONSUMETJ4679.615204.155471.015851.536121.046987.227191.96CONSUMEZJ5764.276170.146217.936521.547020.227952.398713.08表21996—2002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19961997199819992INCOMEAH4512.774599.274770.475064.65293.555668.86032.4INCOMEBJ7332.017813.168471.989182.7610349.6911577.7812463.92INCOMEFJ5172.936143.646485.636859.817432.268313.089189.36INCOMEHB4442.814958.675084.645365.035661.165984.826679.68INCOMEHLJ3768.314090.724268.54595.144912.885425.876100.56INCOMEJL3805.534190.584206.644480.0148105340.466260.16INCOMEJS5185.795765.26017.856538.26800.237375.18177.64INCOMEJX3780.24071.324251.424720.585103.585506.026335.64INCOMELN4207.234518.14617.244898.615357.795797.016524.52INCOMENMG3431.813944.674353.024770.535129.055535.896051INCOMESD4890.285190.795380.085808.966489.977101.087614.36INCOMESH8178.488438.898773.110931.6411718.0112883.4613249.8INCOMESX3702.693989.924098.734342.614724.115391.056234.36INCOMETJ5967.716608.397110.547649.838140.58958.79337.56INCOMEZJ6955.797358.727836.768427.959279.1610464.6711715.6

表31996—2002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19961997199819992PAH109.9101.310097.8100.7100.599PBJ111.6105.3102.4100.6103.5103.198.2PFJ105.9101.799.799.1102.198.799.5PHB107.1103.598.498.199.7100.599PHLJ107.1104.4100.496.898.3100.899.3PJL107.2103.799.29898.6101.399.5PJS109.3101.799.498.7100.1100.899.2PJX108.410210198.6100.399.5100.1PLN107.9103.199.398.699.910098.9PNMG107.6104.599.399.8101.3100.6100.2PSD109.6102.899.499.3100.2101.899.3PSH109.2102.8100101.5102.5100100.5PSX107.9103.198.699.6103.999.898.4PTJ109103.199.598.999.6101.299.6PZJ107.9102.899.798.810199.899.1二、1.输入操作：步骤：(1)FileNewWorkfile0FikeEditObjectViewPro-匚QuickOptionsAdd-insWindowHelpflewWorLcfile...匚trl+N.O.penDatabase...Ctrl+5frogramSaveAs...TertFileClose|Irriport卜步骤：(2)StartdateEnddateK步骤：(3)ObjectNewObjectFileEdit|Object|ViewProc:QuickOptionsAdd-insWindowHelpNewObject...GenerateSeries...ManageLinks&FormuIjse.nFetchfromDB...BRatiSar面0LJpdateselectedfromDB...StoreselectedtoDB…Copyseiected...Renameselected■…DeleteselecttdP_rintSelected步骤：(4)TypeofobjectPoolF2ShowFetchStoreDelete步骤：（5）输入所有序列名称EPool:POOLMODELWorkfile:UNTITLED::Untitled\-nViewIProcObjectPrintNameFreezeEstimateDefinePoolGenrShsetCrossSectidn.Identifierz：tinieridentiEiers"belowthisline)AHBJFJHBHLJJLJ-SJXL忖NMGSDSHsxTJZJ步骤：（6）定义各变量点击sheet—输入consume?income?p?回Pool;POOLMODELWorkfile;UNTITLED;:Untitled\-已scSHsxTJZJ步骤：⑺将表1、2、3中的数据复制到Eviews中2.估计操作：obsCONSUME?INCOME?P?obsCONSUME?INCOME?P?AH-199E3607.430451Z77D109.9000AH-19973693.5504599.27D101.3000AH-19983777.410477（1.470100.0000AH--iggg3901.8105064.60097.80000AH-20004232.9805291550100.7000AH-20014517.6505663.800100.5000AH-2002斗736.5206032400gg.QoaooBJ-19965729.5207332.010111.6000BJ-19976531.8107313.1BD105.3000BJ-19986970.8303471.9SO102.4000BJ-iggg7498.4809132.760100.6000BJ-20003493.4901034969103.5000步骤：（1）点击poolmodel——Estimate对话框说明Dependentvariable:被解释变量；Common:系数相同部分Cross-sectionspecific:截面系数不同部分步骤：⑵将截距项选择区选Fixedeffects個定效应）Cross-section：Fixed得到如下输出结果：□ependentVariable:CONSUME?Method:PooledLeastSquares□ate:07/16/UTime:11:06S-ample:199B2002Includedobservations:7Cross-sectionsincluded:15Totalpool^balanced)observations:105VariableCoefficientStd.Errort-statisticProb.C596.504989.94504-6.839263o.ooaciINCOME?0.6862^20.013S5049.&48B2o.ooaaFiicedEffects(CroesiAH-C-53.23597BJ-C592.4387FJ-C-41.75834HB^C-189.6295.HU-C-192.0^54」L_U0.4-9391&」S-U-^6.60391」X—C-^41.5000LN-C.76302NMG-C-230.1840SD-C-140.3215SH-C227.1060SX-C-9&.131S0TJ-C61.43642ZJ-C230.1580EffectsSpecificationCross-sectionfixed(duinmyvariable^jR-squared0.992490Meand&pendentyar4931.017AdjustedR-squared0.99122&■S.D.dependentvar1700.985S.E.ofregression159.34^6Akaikeinfocriterion1211944Sumsquaredresid2259743.■Schwarzcriterion13.52385Loglikelihood-672.7706Hannan-Quinneriter.13.28332F-statistie754.1521□urbin-Watsonstat1.624146Prob[F-statistic)o.oaaooo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。H2“。模型中不同个体的截距相同（真实模型为混合回归模型）。0iH1:模型中不同个体的截距项«不同（真实模型为个体固定效应回归模型）。1i对模型进行检验:(RRSS-URSS)(4965275-2259743)F—/N-]一=769>F(1490)=18023225974390F-URSS=2259743=769>FO.o514,90)=2259743907(NT-N-K+1)所以推翻原假设，建立个体固定效应回归模型更合理。RRSS求法请参见Eview面板数据之混合回归模型相应的表达式为：Consume—596.50+0.69Income一53.23D+592.44D+...+230.16Ditit1215(6.64)(49.55)R2—0.99,SSE—2259743r其中虚拟变量D，D，…,D的定义是：1215f1,如果属于第个个体，i—1,2,...,15D—<i[0,其他15个省级地区的城镇人均指出平均占收入68.62%。从上面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。时点固定效应模型时点固定效应模型就是对于不同的截面（时点）有不同截距的模型。如果确知对于不同的截面，模型的截距显著不同，但是对于不同的时间序列（个体）截距是相同的，那么应该建立时点固定效应模型：y=丫+屮卩x+uittkkitit(2)k=2时点固定效应模型与个体固定效应模型的操作区别在于步骤（2），将时间项选择区选Period:Fixed（时间固定效应）PoolEstimation£<™firalKr>Options,Esbmnhon理tfriRiyaMethodIS-LeaitSquares(andAR)BalancePoolEstimation£<™firalKr>Options,Esbmnhon理tfriRiyaMethodIS-LeaitSquares(andAR)BalanceDepie*Sample:19^62DD2得到如下结果：DepensentVariable:CONSUME'?Melhad:PaaledlLE^stSquaresDaita:Q7f2i/14TirTie:n：oaSample:19962002lndudl9dob^$rvaUon£：7Crosseecuonsmdude<F15Taialpool{balanced);□bservztiDns105VanableCoefficientSid.Errorl-StaU5ticProb.c-2.63D22560.5&332■0.D3B3520.9695INCOME?0.7300050.0102^47599695a0000FixedEiredts(Period)1986--C11402601997-C137.5006igge-c53.93619-I599-C・30.641272O0D-C-9.0450®2001-0■16002642O02-C・97.74908EiiedsSp^dfic^tionPenddflxed(dummyvanR-squared0.93M39制軸ndepgriMil泊r4981.017MustedR-squared0.985460S.Dctependenlvar1700905S.E.nfregression205.1087月kaikeinfacriierian13.55809sumsquaredresid4000749.Sctiwarzcritennn13.7B030Laglikelihood-7017997Hannan-Ouinn亡『廿皂「13.64003F-sla11sllc1007.^3Durbln-WalsonsialD.736995Pro-Stanslic)0.000000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：s。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（真实模型为时间固定效应回归模型）。1对模型进行检验：t(RRSS—URSS)F二T—1_URSS/(NT—T—K+1)(4965275-4080749)7-1=3.54>F(6,98)=2194080749/0.05/98所以推翻原假设，可以建立时点固定效应回归模型RRSS求法请参见Eview面板数据之混合回归模型相应的表达式为:Consume=—2.6+0.78IP+114D+137.5D+...—97.7Ditititit127(76.0)R2二0・986,SSE二4080749其中虚拟变量DD2,…,D7的定义是：f1,如果属于第t个截面，t=1996,...,2002D=2t[o,其他时点个体固定效应模型时点个体固定效应模型就是对于不同的截面（时点）、不同的时间序列（个体）都有不同截距模型。如果确知对于不同的截面、不同的时间序列（个体）模型的截距都显著地不相同，那么应该建立时点个体固定效应模型：y=X+y+丫卩x+u（3）itttkkitit（3）k=2时点固定效应模型与个体固定效应模型的操作区别在于步骤（2），将截距项选择区域：Cross-section:fixed（个体固定效应）,时间项选择区选Period:Fixed（时间固定效应）PoolEstimationSpEdficaiionPoolEstimationSpEdficaiionOptions得到结果如下：DependentVariable:CONSUME?Method:PooledLeastSquares

Date:07/21/14Time:15:44Sample:19962002Includedobservations:7Cross-sectionsincluded:15Totalpool(balanced)observations:105VariableCoefficientStd.Errort-StatisticProb.C806.6751221.21433.6465780.0005INCOME?0.6533380.03454118.915040.0000FixedEffects(Cross)AH--C-94.50854BJ--C698.0132FJ--C-18.86465HB--C-200.3997HLJ--C-246.3712JL--C-54.16421JS--C-31.26919JX--C-392.9844LN--C47.39508NMG--C-284.2660SD--C-150.8912SH--C465.4906SX--C-152.6560TJ--C103.9569ZJ--C311.5193FixedEffects(Period)1996--C-59.123731997--C17.954691998--C-31.455641999--C-57.240422000--C36.243822001--C-29.264152002--C122.8854EffectsSpecificationCross-sectionfixed(dummyvariables)Periodfixed(dummyvariables)R-squared0.993278Meandependentvar4981.017AdjustedR-squared0.991577S.D.dependentvar1700.985S.E.ofregression156.1067Akaikeinfocriterion13.12288SumsquaredresidSchwarzcriterion13.67895Loglikelihood-666.9514Hannan-Quinncriter.13.34821F-statistic584.0406Durbin-Watsonstat1.455623

Prob(F-statistic)0.000000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。型。HH:九=k=…=九和y==…==0:012N-112T-1对模型进行检验：(RRSS-UR(RRSS-URF=——URSS/(nt-T-N-K+1)(4965275-2022652)/=22-2=5-83>Fo.o52°'83)以推翻原假设，可以建立个体时点固定效应回归模型D个体随机效应回归模型估计截距项选择所以推翻原假设，可以建立个体时点固定效应回归模型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：DeperidentVariable:CP?Method:PooledEGLS(Cross-sectiorirandorrieffects)Date:07/02/08Time:15:06Sample:19962002Includedobservations:7Cross-sectionsincluded:15Totalpool(balanced)observations:105Swsmy日rdAroraestimatorafcomponentvariancesVariableCoefficientSid.Errort-StatisticProb.C345.179575.472174.5735990.0000冋07245690.010572G0.538140.0000RandomEffects(Cross)AH-C-2.553433BJ-C367.0439FJ-C-54.24006HB-C-104.8367HLJ-C-1017680JLY54.90671JS-C-32.27868JZ-223.9519LN-C112.1152NMG-C-133.1377SD-C-100.8713SH-C126.1820SX-C-2279189TJ-C10.08794ZJ-C106.0939

相应的表达式是：CP=345.2+0.72IP-2.6D+367.0D+...+106.1Ditit1215(68.5)R(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=<i|0,其他接下来利用Hausman统计量检验应该建立个体随机效应回归模型还是个体固定效应回归模型。H:个体效应与回归变量（IP）无关（个体随机效应回归模型）0itH:个体效应与回归变量（IP）相关（个体固定效应回归模型）1it分析过程如下：分析过程如下：得到如下检验结果：CorrelatedRandomEffects-HausmanTestPool:POOU02Testcross-secticmrandomeffectsTestSummaryChi-Sq.StatisticChi-Sq.d.f.Prob.Cross-sectionrandom1478756310.0001Cross-sectionrandomRffEctstestcompariscins:VariableFixedRandomVar(Diff.)Prob.IP?0.69766107246690.0000490.0001由检验输出结果的上半部分可以看出，Hausman统计量的值是14.79，相对应的概率是0.0001，即拒接原假设，应该建立个体固定效应模型。检验结果的下半部分是Hausman检验中间结果比较。个体固定效应模型对参数的估计值为0.697561，随机效应模型对参数的估计值为0.724569。两个参数的估计量的分布方差的差为0.000049。综上分析，1996—2002年中国东北、华北、华东15个省级地区的居民家庭人均消费和人金收入问题应该建立个体固定效应回归模型。人均消费平均占人均收入的70%。随地区不同，自发消费（截距项）存在显著性差异。（4）面板单位根检验以cp序列为例。首先在工作文件窗口中打开cp变量的15个数据组。

ShowEnterctls-?fthef-jllo'i'i'ing一：iTLObj巴二tor~Enterctls-?fthef-jllo'i'i'ing一：iTLObj巴二tor~包Seri^5F<:<rrTr£Lalik亡LOGflOor一aofSwi已司Gfoag习==ltl>1一alizt□£单位根检验过程如下：得到如下检验结果：GnuiwunitmcrttESt:Bumtn自"Date:07^02/00Time:15:40Sample:19962002Series:CPAH.CPBJ,CPFJ,CPHE,CPHU,CPJL,CPUS,CPJX,CPLN,CPNMG,CPSD,CPSH,CPSX,CPTJ,CPZJE3(og已nousvariables:Individual巳ffectsAutomaticselectioriofmaximumlagsAutomaticselectiorioflagsbasedonSIC:0to1Newey-WestbandwidthselectionusingBartlettkernelMethodStatisticProb.**Cross-erctionsObsNull:Unitroot(assumescommonunitrootprocess)Levin,LinChu1*9.697781.00001584Null:Unitroot(assumesindividualunitrootprocess)Im,PesarariandShinW-stat6.463801.00001684ADF-FisherChi-square3.049971.00001684PP-FisherChi-square6.243361.00001590Null:Nouritroot(assumescorriirioriunitrootprocess)HadriZ-stat7.583B30.000015105mProbabilitiesforFishertestsarecompuiedusinganasympoticChi-squaredistribution.Allothertestsassumeasymptoticnormality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,CPHU.CPJL,CPJS.CPJX,CPLN,CPNMG,CPSD,CPSH,CPSX,CPTJ,CPZJExogenousvariables:IndividualeffectsAutomaticsale匚ticmofmaMirrumlagsAutomaticselectionoflagsbasedonSIC:□to1Totalnumberofabserrations:B4-Cross-sectionsincluded:15MethodStatisticProb.**Im,PesarariaridShinW-stat6.463801.0000**ProbabilitiesarecomputedassumingasympoticnormalityIntermediateADFtestresultsMaxSeriest-StatProb.E(t)E(Var)L旳L科ObsCPAH6.55420.9999-1.5582.648115CPBJ0.56130.9695-1.5472.33201GCPFJ-0.69690.7711-1.5472.33201GCPHB-0.53040.8U9-1.5472.33201GCPHU2.65880.9991-1.5472.33201GCPJL0.63240.9732-1.5472.33201GCPJS-1.35320.6305-1.5472.33201GCPJX0.53560.9681-1.5472.33201GCPLN1.12760.9893-1.5472.33201GCPNMG3.07660.9992-1.5582.648115CPSD-0.33890.8450-1.5582.648115CPSH0.78710.9754-1.5582.648115CPSX2.11120.9980-1.5472.33201GCPTJ-0.15460.8827-1.5582.648115CPZJ2.00920.9966-1.5582.648115Average1.0653-1.5522.469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2000—2005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）22可支可支可支可支可支可支地配收消费配收消费配收消费配收消费配收消费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6279.49986859.53097702.60298472.65109421.7182104937942国98.0058.0180.8820.9461.10.03.88北103498493115778922124631028138821112156371220176521324京.2.924.60.623.84.840.40.954.20天8140.61218958.69879337.7191103127867114678802126389653津50.0470.225.26河5661.43485984.44796679.50697239.54397951.58199107.6699北16.4782.7568.2806.7731.1809.67山4724.39415391.41236234.47107005.51057902.56548913.6342西内11.8705.0136.9603.3886.1591.63内蒙5129.39275535.41956051.48597012.54198122.62199136.6928古05.7589.6200.8890.1499.2679.60辽5357.43565797.46546524.53427240.60778007.65439107.7369、宁79.0601.4252.6458.9256.2855.27吉吉4810.40205340.43376260.49737005.54927840.60688690.6794林00.8746.2216.8817.1061.9962.71黑龙4912.38245425.41926100.44626678.50157470.55678272.6178江88.4487.3656.0890.1971.5351.01上117188868128839336132491046148671104166821263186451377海.00.804.00.490.34.821.03.033.41江6800.53237375.55328177.60429262.6708104817332123188621苏23.1810.7464.606.57.82浙9279.7020104647952117158713131799712145461063162931225江9.60.086.14.773.74安5293.42325668.45176032.47366778.50647511.57118470.6367徽55.9880.6540.5203.3443.3368.67福7432.56388313.60159189.66319999.7356111758161123218794建26.7408.1136.685.31.41江5103.36235506.38946335.45496901.49147559.53378619.6109西58.5602.5164.3242.5564.8466.39山6489.50227101.52527614.55968399.60699437.6673107447457东97.0008.4136.3291.351

河4766.38305267.41106245.45046926.49417704.52948667.6038南26.7142.1740.6812.6090.1997.02湖5524.46445855.48046788.56087321.59638022.63988785.6736北54.5098.7952.9298.2575.5294.56湖6218.52186780.55466958.55747674.60828617.68849523.7504南73.7956.2256.7220.6248.6197.99广9761.8016104158099111378988123809636136271069147691180东3.27.654.79.949.87广5834.48526665.52247315.54137785.57638689.64459286.7032西43.3173.7332.4404.5099.7370.80海5358.40825838.43676822.54597259.55027735.58028123.5928南32.5684.8572.6425.4378.4094.79重6275.55696721.58737238.63608093.71189220.7973102438623庆98.8409.6904.2467.0696.05.46.29四5894.48556360.51766610.54137041.57597709.63718385.6891川27.7847.1780.0887.2187.1496.27贵5122.42785451.42735944.45986569.49487322.54948151.6159州21.2891.9008.2823.9805.4513.29云6324.51856797.52527240.58277643.60238870.68379265.6996南64.3171.6056.9257.5688.0190.90西7426.55547869.59948079.69528765.80459106.83389431.8617藏32.4216.3912.4445.3407.2118.11陕5124.42765483.46376330.53786806.56667492.62338272.6656西24.6773.7484.0435.5447.0702.46甘4916.41265382.44206151.50646657.52987376.59378086.6529肃25.4791.3144.2424.9174.3082.20青5169.41855853.46986170.50426745.54007319.57588057.6245海96.7372.5952.5232.2467.9585.26、宁4912.42005544.45956067.51046530.53307217.58218093.6404夏40.5017.4044.9248.3487.3864.31新5644.44226395.49316899.56367173.55407503.57737990.6207疆86.9304.4064.4054.6142.6215.52Ec0residEc0resid首先建立工作文件，打开工作文件后，过程如下：□Torkfile:UKTITLED□回丽(View][proclj3btec;i；y[print][SaveDetaile+J-][show]［尸吐d~l扫tOFE］［0曲馆］［8|~1『］［方币灰］Range:Eej'iyObject...DisplayFilter:*Sampl日;CjerLuratuSeries...BreatLinks...FetchfromHE...Updateselected,fromDB...E七0rezelectedtoHE...Copyselected...尺凯釦电selecteii...Beleieeelecie>1Frint£已lected<|>f\UintitlodXNewPage/建立面板数据库，并命名为XY。NevObjectTyre<i£objectPoolEquationGraphGroupLogLMatrix-Vectoh■—匚oe:ModelPadSampleSeriesSeriesLinkiAlphaSSpaisS75temTaBleTextValM^pVAK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