STATA面板數(shù)據(jù)模型操作命令要點(diǎn)

1、STATA面板數(shù)據(jù)模型估計(jì)命令一覽表一、靜態(tài)面板數(shù)據(jù)的STATA處理命令固定效應(yīng)模型隨機(jī)效應(yīng)模型y二a+xB+8itiitity二xb+卩ititit卩+8ititit（一）數(shù)據(jù)處理輸入數(shù)據(jù)tssetcodeyear該命令是將數(shù)據(jù)定義為“面板”形式xtdes該命令是了解面板數(shù)據(jù)結(jié)構(gòu).xtdescode:1,220n=20year:20CM,20057?014T=11Delta(year)=1unitspan(year)=11)eriods(code*yearuniquelyidenfif1eseachobse門)Distrrbufio門ofT_1:min5%25%50%75%95%max111

2、11111111111Freq.Percentcum.Patter門20100.00100.001111111111120100.00 xxxxxxxxxxxsummarizesqcpiunemgse5ln各變量的描述性統(tǒng)計(jì)（統(tǒng)計(jì)分析）.summarizesqepiunemgse51nunem95e5InobsMean.91427981.10655.0349455.1Q907.02665412.930346.032496.0071556.042752B.0116719Min4.75e-O61.045.0120246.0053Max26.22B0L1.25.046.2B57.0693220.12

3、19364.0240077genlag_y=L.y/產(chǎn)生一個(gè)滯后一期的新變量genF_y二F.y/genD_y=D.y/genD2_y=D2.y/產(chǎn)生一個(gè)超前項(xiàng)的新變量產(chǎn)生一個(gè)一階差分的新變量產(chǎn)生一個(gè)二階差分的新變量（二）模型的篩選和檢驗(yàn)1、檢驗(yàn)個(gè)體效應(yīng)（混合效應(yīng)還是固定效應(yīng)）（原假設(shè)：使用OLS混合模型）xtregsqcpiunemgse5ln,fe.xtregsqcpiunemgse51n,feFixed-effects(within)regressionNumberofobs=220ojpviiriable:codeNumberofgoups=20Fl-sq:withdn=0.2307o

4、bspergoup:min=11between=0.0767avg=11.0aver捫1=0.0064max=11F(5,195)=11.69corr(u_i,xb)=-0.5296ProbaF=0.0000sqcoef.std.Err.tP|t|95%Cerviil匸固應(yīng)模型而，回歸結(jié)最后行匯報(bào)統(tǒng)計(jì)量H檢驗(yàn)所nein-187.62948.30219-3.&8G.000-92.36726g-6.3666843.93778-1.620.108-14.132791.B99421效5體上顯著在我們這例子發(fā)現(xiàn)f量的概率In20.2856712.302961.650.101-3.97828

5、544.54963果表表定效應(yīng)模優(yōu)于混合。:.-i-?-10.59&2211.31392sigma_u2.6887963sigma_e2.0017081rho.64340771(fracfionofviiriancedueto2、檢驗(yàn)時(shí)間效應(yīng)（混|效應(yīng)還是隨機(jī)應(yīng)（原假設(shè)：使用OLS混合模型）quixtregsqcpiunemgse5ln,re(加上“qui”之后第一幅圖將不會(huì)呈現(xiàn))xttestO.JttrEgsqcpiiunemgseS1門，reRandom-effectsGLSregressionNumberofobs=220右roupvmriable:codeNumberofgroups=

6、20R-sq:within=0.2206obsprgroup:min=11between=0.0376avg=11.0ovEral11=0.0276max=11waldchi2(5：；=47.10carr(u_i,x)=0(assumed)Probachi2=0.0000sqcoef.std.Err.zP|z|95%Cervalcpi4.40744E4.9206440.900.B70-5.23683714.05173unem-114.230440.79262-2.800.005-194.1825-B4.278BB-B.10BB793.910586-2.070.OBB-15.768

7、49-.4392721se577.5970415.133895.130.00047.93516107.25B9In5.825365ID.939920.530.594-15.6164B27.26722_cons-1.BS11B5.49937B-0.340.732-12.659768.897403sigma_u1.965&592sigma_e2.0017081rho.49096525(fracfionofvmrianceduetou_i)xttestOEreuschandPaganLagrm門gTii門multipl1ertestforrandomeffectssqcode?t=xb+ucode+

8、ecode?tvmrsd=sqrt(var)sq8.5869252.930346p值為朗5隨機(jī)效到值為46%明隨機(jī)效模型I于混chibar2(01)=193.03probchibar2=0.GOODEstimatedresults:3、檢驗(yàn)固定效應(yīng)模型or隨機(jī)效應(yīng)模型（檢驗(yàn)方法：Hausman檢驗(yàn)）原假設(shè)：使用隨機(jī)效應(yīng)模型（個(gè)體效應(yīng)與解釋變量無(wú)關(guān)）通過(guò)上面分析，可以發(fā)現(xiàn)當(dāng)模型加入了個(gè)體效應(yīng)的時(shí)候，將顯著優(yōu)于截距項(xiàng)為常數(shù)假設(shè)條件下的混合OLS模型。但是無(wú)法明確區(qū)分FEorRE的優(yōu)劣，這需要進(jìn)行接下來(lái)的檢驗(yàn)，如下：Stepl：估計(jì)固定效應(yīng)模型，存儲(chǔ)估計(jì)結(jié)果Step2：估計(jì)隨機(jī)效應(yīng)模型，存儲(chǔ)估計(jì)結(jié)果

9、Step3：進(jìn)行Hausman檢驗(yàn)quixtregsqcpiunemgse5ln,feeststorefequixtregsqcpiunemgse5ln,reeststorerehausmanfe(或者更優(yōu)的是hausmanfe,sigmamore/sigmaless).hausmanfecoefficientsI(b)fe(B)re(b-B)Differe門sqrt(diag(v_b-v_B)E2.9216984.407448-1.48575unemI-187.629-114.2304-73.3966325.86627gI-6.366684-8.1038791.737196.4619B27s

10、e5178.2397977.59704.64275582.06747220.2&5675.&2536514.460B15.628595b=consistentunderHoandHa;obtainedfromxtregB=inconsistentunderhr,efficientunderHo;obtainedfrom看出檢驗(yàn)的值為.0000拒絕原Chi2(5?=滿足需用工具變量法probch-i2=0.0000(v_b-v_Bisnotpositivedefinite)（三）靜態(tài)面板數(shù)據(jù)模型估計(jì)1、固定效應(yīng)模型估計(jì)（如下圖所示）.xtregsqcpiunemgse51n,feFixed-ef

11、fects(within)regression右roupviiriable:codeR-sq:within=0.23Q7between=0.0767veral1=0.0064corr(u_-i.Xb)=-0.5296Numberofobs=220Numberofgroups=20obspergroup:mln=11avg=11.0max=11F(5,155：；=11.69ProbaF=0.0000coef.std.Err.tP|t|95%conf,intervalcpnnemcons2.921698-1B7.629-6.3666E478.23979表明我們.35785124.8795974E.

12、B02193.9377815,27446用的是5.5552410.60-3.88-1.625.12效應(yīng)0.060.5500.0000.10E0”O(jiān)DD型，表0.949-6.701S63-282.890E-14.132794811545部分的前-10.5962212.54526-92.B6726B99421108.B641呈現(xiàn)了31392sigma_usigma_e3行到2.68879632.00170815行示了模型擬合優(yōu)度1、分為組內(nèi)、組間Ftestthat,alu_i=0:F(19?195；ProbF=0.0000 xtregsqcpiunemgse5ln,fe型中所有非常數(shù)變量執(zhí)行聯(lián)合檢

13、驗(yàn)得到的F統(tǒng)計(jì)量和相應(yīng)的P值，可以看出，參數(shù)整體上相當(dāng)顯著。需要注意的是，表中最后一行列示了檢驗(yàn)固定效應(yīng)是否顯著的F統(tǒng)計(jì)量和相應(yīng)的P值。顯然，本例中固定效應(yīng)非常顯著。2、隨機(jī)效應(yīng)模型估計(jì)若假設(shè)本例的樣本是從一個(gè)很大的母體中隨機(jī)抽取的，且d與解釋變量均i不相關(guān)，則我們可以將d視為隨機(jī)干擾項(xiàng)的一部分。此時(shí)，設(shè)定隨機(jī)效應(yīng)模型i更為合適。xtregsqcpiunemgse5ln,re（如下圖所示）.xtregsqepiunemgseS1門,reRandom-effectsglsregression右roupVHriable:codeNumberofobsNumberofgroups22020R-sq:

14、withinbetweenverill10.22060.03760.0276obspergroup:min=avg=max=1111.011corr(u_i,x)0(assumed)waldchi2(5：Probachi247.100.0000coef.std.Err.95%conf,interval95ne1s3、時(shí)間固定效應(yīng)IIK1sigma_usigma_eFilQ4.407448-114.2304-8.10387977.597045.&253658B118.96585922.OO17OE14.92064440.792623.9105E615.1338910.939925.4993780

15、.90-2.80-2.075.IB0.53-0.詔.370.005.OBB.000.594.732-5.236837-194.1E25-15.76E4947.93516-15.61648-12.6597614.05173-34.27833斗392721107.256927.267228.&97403ffract-onofvar1ancEn；：toi/.J)tabyear,gen(dumt)先，我們需要定義一下T-1個(gè)時(shí)間虛擬變量。(tab命令用于列示變量year的組類別，選項(xiàng)gen(dumt)用于生產(chǎn)一個(gè)以dumt開(kāi)頭的年度虛擬變量)dropdumtl(作用在于去掉第一個(gè)虛擬變量以避免完全共線

16、性)ooooooooooo333333S33H9.0918.1827.2736.3645.4554.5563.64為Kei.8290.91100.00.tabyear,qen(duint)year|Freq.Percentcum.2004200520062007200820092010效應(yīng)模2012np9mpTotal220100.00dropdumftlxtregsqcpiunemgseS1ndumrt*,feNumberofobsNumberofgroups22020R-sq:withinbetweenover10.28380.02610.0935obsprgroup:minavgmax1

17、111.011torr(u二，xb)-O.1752F(15,185)ProbaF4.B90.0000sqCoef.std.Err.PA|t|95%Conf,intervalepiunemdddddddd95n2345678_ysemftmftmftmftmftmftmftmft3.774834-37.000673.11105277.3662613.0311230105783376552.1626971.559&965.4316914.42328291.08E1071.6057511.9060542.22063-6.6810484.96187264.586285.0B889515.7002813

18、.B2385.6316355.6347213.6458275.6473164.6733501.6448627.6B46403.7724666.795677.90306375.9227990.76-0.570.614.93O.98-0.48-0.530.25O.860.640.6659082.402.46-1.13.448.567.542.000.329.634.595.801.388.522.512.114.039.018.015.261-6.014293-164.421-6.92867646.39164-IB.25511-1.54719B-1.589878-1.111437717174589

19、67408B4894742625997.0B17745.3362871.4390025-18.3659613.5639690.4196613.15078108.340939.31735.9450769.91456751.4368311.8369681.7601241.6955132.4388133.1297273.4758224.0022575.003865sigma_usigma_erho2.19645051.982827.5509617B(fractionofvarlanceduetou_1)（四）異方差和自相關(guān)檢驗(yàn)I85)10.75ProbF=0.0000Fixea-effects(wi

20、thin)rgression石roupvnriable:code1、異方差檢驗(yàn)（組間異方差）本節(jié)主要針對(duì)的是固定效應(yīng)模型進(jìn)行處理檢驗(yàn)原假設(shè)：同方差需要檢驗(yàn)?zāi)Ｐ椭惺欠翊嬖诮M間異方差，需要使用xttest3命令。quixtregsqcpiunemgse5ln,fexttest3.qu*ixtrgsqcpiunemgseS1n,fe.xttest3ModifiedWaidtestforgroupwiseheteroskediisfiHtyinfixedeffecrregresslonmodelHO:sigma(i)2=sgm?forall1ch-i2(20)=11561.60Probch-i2=0.

21、0000顯然，原假設(shè)被拒絕。此時(shí)，需要進(jìn)一步以獲得參數(shù)的GLS估計(jì)量，命令為xtgls：xtglssqcpiunemgse5ln,panels(heteroskedastic).xtglssqcpiunemgse51n,panels(heteroskedastic)cross-sectionaltime-seriesFGLSregressioncoefficients:genera!ized1eastsquaresPanels:heteroskedasticCorrelation:noautocorrelationEstimatedcovaritrices20Estimatedautocorr

22、elations=EstimatedcoefficientsNumberofobs=220Numberofgroups=20Timeperiods=11Waldchi2(5)=19.79Probachi2=0.0014sqcoef.std.Err.zP|z|95%Cervalcpi1.5637921.771160.880.377-1.9076175.035201unem異，&差通選項(xiàng)來(lái)設(shè)定。述結(jié)果是兩步獲1組9一異方差通選項(xiàng)來(lái)設(shè)定。述結(jié)果是兩步獲10.70095.9898711.790.074-1.03902922.44083用L估計(jì)不考異方差|型，利用殘差計(jì)算。并最終_cons

23、.02029121.8717450.010.991-3.6482623.6888442、序列相關(guān)檢驗(yàn)對(duì)于T較大的面板而言，Q往往無(wú)法完全反映時(shí)序相關(guān)性，此時(shí)便可能iit存在序列相關(guān)，在多數(shù)情況下被設(shè)定為AR(1)過(guò)程。原假設(shè)：序列不存在相關(guān)性。(1)FE模型的序列相關(guān)檢驗(yàn)對(duì)于固定效應(yīng)模型，可以采用Wooldridge檢驗(yàn)法，命令為xtserial:xtserialsqcpiunemgse5ln.xtserialsqcpiunemgse51nWooldrrdgEtestforautocorrelationinpaneldataHO:nofTr呂torderautocorrelafionF(1,1

24、9)=1246.120ProbaF=0.0000可以發(fā)現(xiàn)，這里的P=0.0000，我們可以在1%的顯著性水平下愛(ài)拒絕不存在序列相關(guān)的原假設(shè)?？紤]到樣本，該檢驗(yàn)的最后一步是用對(duì)進(jìn)行OLS回歸，eeiti，t-1因此，輸入以下命令得到。檢驗(yàn)該值是否顯著異于-0.5,因?yàn)樵谠賕=0.8858設(shè)下（不相關(guān)），可見(jiàn)本例中不相等，拒絕原假設(shè)，說(shuō)明存在序列相關(guān)。g=-0.5matliste(b).mat11ste(b)symmetri匸e(b)1,1L.000006yl.8B5B2499（2）RE模型的序列相關(guān)檢驗(yàn)對(duì)于RE模型，可以采用xttestl命令來(lái)執(zhí)行檢驗(yàn):quixtregsqcpiunemgse

25、5lndumt*,rexttestl.quixtrEgsqcpiunemgse51ndunrt*，史.xttestlTestsfortheErrorcomponentmodel:sqcode,t=xb+code+vcode,tvcode,t=1ambdavcode?(t-1)j+ecode,tEsfimatedresults:茁sd=呂qrt(Var)sq8.5869252.930346eB.9B160B1.9E2E27uB.B714421.9675979Tests:RandomEffects,TwoSided:報(bào)了4個(gè)統(tǒng)計(jì)別用U檢驗(yàn)?zāi)Ｐ椭须S機(jī)效應(yīng)（RandomEffects,OneSided

26、:及二者的聯(lián)合顯結(jié)果存在隨機(jī)應(yīng)和序和序列相關(guān)的聯(lián)合檢alm(1ambda=0)=非常顯著。1卜m；0.0000Jcr門tTest:型估計(jì)./；=ij.1三-bdc.=i；chi2(2)=0.0000上述結(jié)果表明，無(wú)論是FE還是RE模型，干擾項(xiàng)中都存在顯著的序列相關(guān)。為此，我們進(jìn)一步采用xtregar命令來(lái)估計(jì)模型，首先考慮固定效應(yīng)模型:xtregarsqcpiunemgse5lndumt*,felbixtreqarsqcpiunemgse51ndumrt*sfelbiFE(within)regressi0-inwithAEi(l)chstuirbaincESNumbeofobs=200Grou

27、pvariable:codeNumbeofgroups=20R-sq:within=0.1836between=00070overal1=0.0572obspergroup:min=avg=max=oo0.1corr(u_i.xb)00108F(14,166)ProbaF2670.0015coef.std.Err.tPA|t|95%Conf,intervalcpiunemseSIndumrt2dumrtB117932-9.3173121.0E0344.333969737480331779575680674.959322320.0874B1.2061565.0811354.1B0462.1123

28、098.1500541210.90810_.902.643.372.395.860.005.000201197748.977181.3010435698009&991247539535586432791.77611330.34256戈46172314.365957516286一06056-.2718068-testthatal1u_i=0:nodifIedBhargsivsietal.3altag-i-WuLBI=.641462IdumrtsdumtgF(19,166)=DurBrn-watson17.43.219&3495ProbaF0.000(58419153320582.189307.1

29、5B31223、“異方差一序列相關(guān)”穩(wěn)健型標(biāo)準(zhǔn)誤0.0020.0320.6336347512285797521043190293652.17425340.0005.9276796.229229雖然上然上sigma_erhc.frjv.943S6988M方法在.49972224者立一起考慮，要獲得“異!差5序列相關(guān)Fix.ed-effects(within)regressionGoupveutiable:codeNumberofobsNumbeofgoups22020R-sq:within=02307between=0.0767veall=0.0064obspergiroup:imin=avg=

30、max=1111.011coir(u_isXb)=-05296F(5,19)PobAF2520.0656(stdEadjusted1Fo20incode)cpiunem95ne1sRobustcoefstdEP|t|95%Confinterval292169S-107629-6.3666847E2397920.2856735785123.83786706419殳9709155呂1207711.421564117750.76-2：13-1.601351兀0.09.456046.125.194092932-5111051-371.94951L46779-43.408B7619918109-5445

31、-3.308556:L94453719988844191268.976401,只需在xtreg命令中附加vce(robust)或者vce(cluster)選項(xiàng)即可。例如，對(duì)于FE模型，我們可以執(zhí)行如下命令：xtregsqcpiunemgse5lnfevce(robust)xtregsqcpiumenngse5lnsfevce(robust)sigma_usigma_erho2688796320017081(fractionofvariamceduetou_i)64340771與之前未經(jīng)處理的估計(jì)結(jié)果相比，附加命令vce(robust)選項(xiàng)時(shí)的結(jié)果，雖然系數(shù)的估計(jì)值未發(fā)生變化，但此時(shí)得到的標(biāo)準(zhǔn)誤

32、明顯增大了，致使得到的估計(jì)結(jié)果更加保守。對(duì)于面板數(shù)據(jù)模型而言，STATA在計(jì)算所謂的“robust”標(biāo)準(zhǔn)誤時(shí)，是以個(gè)體為單位調(diào)整標(biāo)準(zhǔn)誤的。因此，我們得到的robust”標(biāo)準(zhǔn)誤其實(shí)是同時(shí)調(diào)整了異方差和序列相關(guān)后的標(biāo)準(zhǔn)誤。換言之，上述結(jié)果與設(shè)定vce(cluster)選項(xiàng)的結(jié)果完全相同。4、截面相關(guān)檢驗(yàn)原假設(shè)：截面之間不存在著相關(guān)性FE模型檢驗(yàn)對(duì)于FE模型，可以利用xttest2命令來(lái)檢驗(yàn)截面相關(guān)性：quixtregsqcpiunemgse5ln,fexttest2(該命令主要針對(duì)的是大T小N類型的面板數(shù)據(jù)，在本例中無(wú)法使用，故圖標(biāo)略去。)RE模型檢驗(yàn)對(duì)于RE模型，可以利用xtcsd命令來(lái)檢驗(yàn)截面

33、相關(guān)性：quixtregsqcpiunemgse5ln,rextcsd,pesaran(下面命令是另一個(gè)檢驗(yàn)指標(biāo))xtcsd,freesq說(shuō)xtregsqcpiunemgse51nsrextcsdspesairanPesaran芻testofcrosssectionalimdepemdence=-0348,Pr=1.2725xtcsdSfireesFreestestofcrosssectionalindependence=1.255I1criticalvaltiesfromFreesQdistribullonalpha=0.10:0.2333alpha=0.05:0.3103alpha=0.0

34、1:0.4649可以看出，兩種不同的檢驗(yàn)方法均顯示面板數(shù)據(jù)存在著截面相關(guān)性。5、“異方差一序列相關(guān)一截面相關(guān)”穩(wěn)健型標(biāo)準(zhǔn)誤(1)FE模型估計(jì)10.71696-131.68977169419111.850534.010649.OO2B35對(duì)于FE模型，在確認(rèn)上述存在著截面相關(guān)的情況下,我們可以采用Hoechle(2007)編寫的xtscc命令獲取DriscollandKraay(1998)提出的“異方差一序列相關(guān)截面相關(guān)”穩(wěn)健型標(biāo)準(zhǔn)誤：xtsccsqcpiunemgse5ln,fe.xtsccsqcpiunemgse51n,feRegrEswio門withDriscol1-Kstandsrder

35、rorsNumberMethod:Fixed-effectsregressionNumber右roupvmriable(i):codeF(5,max-imum1ag:2ProbwithinfofgroupsobsF=R-squared2202028.200.00000.2307Drisc/Kraaycoef.std.Err.tP|t|95%conf,intervalunem95ne1s2.921698-187.629-6.B666B47B.2397920.28567.B5785124.17B78325.9562.23026820.900B55.1239924.5632780.70-7.23-2

36、.853.743.960.08.492.000.010.001.001.938-5.814129-241.9556-11.0346934.49389.561034-9.193211.65753-133.3Q25-1.69E68121.9B5831.010B19.90E903這里，xtscc命令會(huì)自動(dòng)選擇的滯后階數(shù)為2,系數(shù)估計(jì)值和Within-R2與xtreg,fe的結(jié)果完全相同，但標(biāo)準(zhǔn)誤存在著較大差異?？梢?jiàn)，在本例中，截面相關(guān)對(duì)統(tǒng)計(jì)推斷有較大的影響。若讀者有跟高的方法來(lái)確定自相關(guān)的滯后階數(shù)，則可以通過(guò)lag()選項(xiàng)設(shè)定。當(dāng)然，在多數(shù)情況下，這很難做到。不過(guò)我們可以通過(guò)附加lag(0)來(lái)估計(jì)僅

37、考慮異方差和截面相關(guān)的穩(wěn)健型標(biāo)準(zhǔn)誤，命令如下：xtsccsqcpiunemgse5ln,felag(0).xtsccsqcpiunemgseS1n,felag(O)RegrEssVo門withDriscol1-KraaystandarderrorsMethod:Flxed-effectsregresslonDroupviiriable(1):codemaximum1ag:0NumberNumberF(5.ProbawithinofofgroupsobsFR-squared2202018.960.00000.2307Drisc/Kraaycoef.std.Err.tPA|t|95Conf,intervalcpiunemg5ne1s2.921698-187.629-6.3666B478.2397920.28567計(jì)(略3.72440226.726562.6993216.058456.557483補(bǔ)充)0.78-7.02-2.364.873.090.09.442.000.029.000.006.932-4.873565-243.5684-12.0164344.629086.560703-8.287132二、動(dòng)態(tài)面板數(shù)據(jù)的STATA處理命令（一）差分GMMxtabondl