实验五七虚拟变量回归分析

第七章虚拟变量回归分析姓名：耿肃竹学号：20136878班级：经济1302【实验目的】目的在于学习基本的经济计量方法并利用Stata对经济中典型的数据，掌握虚拟变量的分析思路，掌握虚拟变量回归的基本操作方法，掌握虚拟变量回归的结果分析。【实验软件】Stata是一套提供其使用者数据分析、数据管理以及绘制专业图表的完整及整合性统计软件。该软件提供的功能包含线性混合模型、均衡重复反复及多项式普罗比模式。作为流行的计量经济学软件，Stata的功能十分地全面和强大。可以毫不夸张地说，凡是成熟的计量经济学方法，在Stata中都可以找到相应的命令，而这些命令都有许多选项以适应不同的环境或满足不同的需要。【实验要求】利用stata软件学习多元回归分析的应用问题，并在回归结果中学会以下命令的使用对类型变量B生成虚拟变量AtabulateB,gen(A);对包含虚拟变量的情况进行回归regressyx1x2...A2A3...等命令。学会虚拟变量在回归分析中的应用进行有效分析，学以致用。【实验内容】教材P213——C2题目【1】C2(I)输入命令“regressIwageeducexpertenuremarriedblacksouthurban”:

.r&g-r-eaaL^ra.g&edus巳陛亡監fcen.urieiuarri-&dhlasla^uthurl>anSourceSSdfMSNmnti-eEctfobs=935rIfm」ItModel叫837761975_97682312Frnt)aF=a-onaakesidual123„S185219-27.1335五于叮五訂R-squared=a.2526AljR-squared.U--£40JIctal165.五5五2為訂9-34.177362188RcctMSE.36547IwagsCfl-ef.Std.Err-tp>l11[95tCnnf„IntSEV31]educ-0654207.006250410„47a_□□□„05216420776973._Q14042.00318524„4La.□□□„007792.020294tenure-□117472.0024534„79a.□□□„OQGS333Q1€E€13married..1994171.03905025„1La.□□□„1227S01.27€0E4black-.1662495-5„oaa.oaa-„2€22717—■1144281south.-.a&as-as^.0262485-3_46a.not-„142417—■D3939D3urban.LS39121-026S583a-口□□„1310056-236S1S5czcms旨.395497.11322547„65a-口□□丐.173295.617704解：log(wage)=5.395497+0.0654307educ+0.014043exper+0.0117473tenure(0.113225)(0.0062504)(0.0031852)(0.002453)+0.1994171married-0.1883499black-0.0909036south+0.1839121urban(0.0390502)(0.0376666)(0.0262485)(0.0269583)n=935R2=0.2526保持其他因素不变農人和非黑人之间的月薪差异近似（约等于）为0.1883499，因为P=0,所以这个差异是统计显著的。(口)输入命令”generateexpersq二exper*exper”“generatetenuresq二tenure*tenure”“regressIwageeducexpertenuremarriedblackurbanexperaq

urbanexperaqgaiiersteexpersq=exii-e=rqan£rstetsiiuresg=t&nure^tenur-aF&g'^-essedueg卫h&®temir-eblacts^uthuurbantetiuT&sgSOLIECZE5SdfMSHuinberofa-bs=525itroon-h—QF1"7—m.丄*Model42-235325794.S&2S13S7?H£>b>F=o.aoooEtesid.ua1123.420558525.12342Safi2Et—scjuared=0.2550—HArr比d~lKEd.—U-ZffTotallfi5„fi5€283934.1773C21®©=_3£52®Co-ei.日匸lZXE■tF>lt1[95%Conf.IntsEvsl]educ„D642761„a063115ia.18a_aoa.0518896„D766625eitper„DL72146-□12613B1.36a.1730075403„0419695tenu匚e-□243-291_noa12373.D7a.002.0089743^□408838married.158547„a391103a_aoa.1217917„2753023black-..19a6：636„a377011-占・□石a_aoa-.2«46533-.116674south-..□912153„0262356■宕.屯Ea_aoi-.1427035-.□397271urban..1854241„as69585€.88a_aoa.1325L71-2381311B-Kpetaq-..□□anas„aaa53is-□.-.21a„83i-.0011576.□aassteTLuregg-„aaa79€4.000471-1.69a_asi-.0017208„aaai2?s_eans5.258C7G_12591-4342.呂石a_aoa5„1115G55.605787输入命令“teseexperaq”：teatejcp&rsgtsjiiiT&sg(1)ezjcpersg=0(2)t-enureaq=□F(2_925)=1„43Prob>F=0_2260解：因为P=0.2260,不拒绝原假设，所以即使在20%的显著性水平上，它们也是联合不显著的。（皿）输入命令“generateblack_educ二black*educ”“regressIwageeducexpertenuremarriedblacksouthurbanblack_educ”：

.g&n.&r&tebLa.d_&duc=b-l^QlSi.r&g-r-eaa丄耐ng~旨i&duQap咎~rtenuremariri&clblacla^uthurbanSourcessd=MSNuzibe匚口三ctbs=9-35■ELQrn匚0、—口工.口EModel42..005546885.25069335Fno-b>F=0.0000kssldual123.石5口73«926：.133532113R-squaxed=0-2536AdjR-squared=0-2471Ictal165.石5丘334.177362186RactUSE=.3fi542luageCoe=.Std.Zir-tp>l11[9齐Conf.Intecval]adue.□GILLS2.006427710.440.000.O54SOO0.□797298-血3O2E9.00319DC4.320.000.0075642.□2D0S76.□11787.00245294.610.000.00«5732.OdLGGDD9ztazried..19e&077.029-04745.050.000.1222761.2755292black..□94308625529S40.370.711-.4a«42D2.5960275south.-.0694495.02£27^5-2.40o.oai-.14101E7-.0218802uzban.1S3&E23.02G9E47C.B20.000.130953.22石bla.ckeduc-,a22S22fi.0201627-1.120.263-.063232fi,aiS&S54consE.274S17.11470274嵩.DC0.ooc5.149715.55&^25解：因为P=0.263，不拒绝原假设，所以扩展原模型，使受教育汇报取决于种族，然而受教育的回报不取决于种族(IV)输入命令“tabulatemarried,generate(married)”“regIwageeducexpertenureaouthurbanmarried*black1”：tabul邑t;色b1ack『y-esn-erate(blacU=1izblackFra<q.PercentCum.081587.1787.17112012..83laa.aaTotal935ioa_aatabulatemarriedg-ene^ats(marri&d)=1LffflSEEl&dFt旳.PercentCum.0ioaia.7a10-70182E89_aaT口七ml92Eioa_aaregJ■珏rag*-educexpertermire：southur-baiLma-Firiedl*blad1generateblackL_nariried2=black1*ma.rried2.generate匕丄acJSZ_marrledl=BlacJtZ^naarrl&dl.generateblack2_marri&d2=black2*marriiad2.regresslwageeducexpertenureso-^Ath.utbanblackl^arri&d2black2_jaarri&dlblack2_raarried2SourceSSdzMSNmnherofoha=935F（8P926139.17Model41„88493595.23561699Erob>F=a.ao口口ResidualLZ3.7713^7.13366Z36ZR-squared.-□-25ZBjD.—sqxiAEed"Total165.656293934.1773621SSRootMSE-.3656IwageCce£.Std.2匸匸.七A|tITcitiEEvcl]educ.□654751.□0625310.47aao.0532034.07774^9exper.0141462.□021914.43口ao卫口78837.0204087tenure.011^628.00245790.000.00€@39aauth.-.□91&S94.0262212-3.4&a,aoa-.0403^33urban.18435口1.0269778€.83口ao.1314口53.2372948hlaaklmarried2.1883147.04287774.41口ao.1047S59-2730635hlack2_marriedl-.24082.0960229-2-51口_ai2-.42S2677-.0523723l^lackZmarzled2.□□9^484■口耳昨口131口-1T口-呂百右--1CICI478弓-115S7E7_con95.403793.114122241.350.0005.1198255.€27762输入命令"display0.0094484-0.1889147”:.Jlieplay0.00344S4-D.18S3147-.17S46G2解：（1）以未婚黑人（即black1_married1）为基组。（2）定义已婚黑人、未婚非黑人、已婚非黑人三个交互项。（3）估计已婚黑人和已婚非黑人之间的工资差异为17.94663%题目【2】C4（I）解：（1）预计：p1v0,p2>0,p3v0,p4>0,p5>0,p6>0（2）02没有把握，因为它是二次项系数。（口）输入命令"regresscolgpahsizesqhspercsatfemaleathlete”:iregr-esscolgpabistisizeaqhsperciregr-esscolgpabistisizeaqhsperc：female：athlete色目幻色目幻hsisesqh.spsresa.tF是m昌LeathlefcsSfiUECfiSSMSar=4127F(「4130)284.59Model524_83_9-305G87.46»ee42Prob3F=□.0000E2.esid.ua112€9_276374120-207955053R—aq^iared.=□.292EAdjR—squared0.2915Total1754.A9-5€7.422759726Roo-tMSE=.5544colgpaCo-ef.St^i.Err_七2|t|[95%Conf.InteE-ira1]Kaxze0569543-QL6：3513-2.48口.001asgsii?-^□24796®.□□224S42.DE口.Q2£・Q叮a2G54.□□90854hspscc-.D13212石.□□a572S-23・Q7口■口叮口-.0143355-.□12089=6"匸.□aao€€£24.44口■口叮口・Q叮15154.□□1777-4feiriBle.1548814-01800478„«□Q_0(]Q-1195026_1901802athlete.1693064-04234924.00Q_0(]Q-D862791.2523336_CQHS1-241365-079492315„62□_0(]Q1.0855171-397212解：（1）以形式报告结果:colgpa=1.241365-0.0568543hsize+0.0046754hsizesq-0.0132126hsperc(0.0794923)(0.0163563)(0.0022494)(0.0005728)+0.0016464sat+0.1548814female+0.1693064athlete(0.0000668)(0.0180047)(0.0423492)n=4137R2=0.2925(2)估计运动员与非运动员之间GPA的差异是16.93064%，t=0.1693064/0.0423492=3.998，是统计显著的。(皿)输入命令“regresscolgpahsizehsizesqhspercfemaleathlete”:

SGUECeSSq£口上旧=：4137191.9-2Model338-2171235676434247FrobaF=：n_aaaaB.e3id.ual1455„57855413135245184R-squared=：0.1885AdjEt-aijuaredn.1875Ictal1794„19567413C.433753728jlcctUSE_5M68colgpaCoef_St-d.Eee-tF>l11[96%Conf.Interval]-.□524038.0175092-2.05□-002-.0877212—.E19D7G3halssaq-0053228.ao24aeG2_21□-027.aaaeoQ?.□iaa<5hspencz-.□L713«5.0005892-29.09-□_aaa-.ais23ifi--0159S14female-□581221.01881622.09-□.□□2.0212222„095013athis七电.□□54487.0447871□.120.903-.0823582-092255€czcms3_047ess.□32914852„59^o.oaa2.9-031C73.112229解：（1）从模型中去掉sat后，athlete的系数变为0.0054487，标准误变为0.0447871，从实际上和统计上都是不显著的（2）因为模型中没有控制变量sat，第（2）小题在模型中加了sat，运动员的表现比非运动员的表现好，此时虽然没有控制sat变量，结果仍然一样（IV）输入命令"tabulateathlete,generate（athlete）”：tatju.1岂七良a.t-h.1e匕p』召■曰九总ra■七mel匕hl亡七=1ifathleteF匚eq：.Fee"匚UKltCusi»0S5.2111944.69100.00IDtfll4,137100.00tabu.1atefaua.1e「gen-&rate(fejna_l&5=1iffemaleFesii.PercentCum.□2,27755.04E5.CM11,86044.36100.00Iatal4,137100.00输入命令"regresscolgpahsizehsizesqhspercsatfemanonath”:aQQlgpaIxaia-iehais-eaqixape-r?aa~t£"曰g&n-erat-ef&naath=feraalel*athl^teZg&n-erat-emalenonath=femal&2*athlet&lg&n-erate血了1eath=*athl£ts2•是目目s^lgpahai5-@：hsp&rc目fs^aathraalitft^iiathL血吕].色吕怛已Sctirefid=MSNuiTilseEofabs口jtjnn.4=4127—OAQ口!Drqppp丄丄3dModel524„821272■774.g?44fi?4Prob>F=o.aaaaKea£d.nal12G9.97444129.207429Q5_5R—sqxiare-d=0.2925啊j_■i例_j工口七ml1754.15567419«.439735726冬口口七MSE=„5544€colgpaCo£f.吕七且.Err.ttl[SE-%Con^.Irit;er-iral]haiaeosesoafi.01€3fi71-3-47Q.OC1-.qssssss-.0247124hsiaeaq.0022SQ72.07Q.03S.QC02-573.aQ9Ua25hapsc匚-.□13211^.0CQ573-23.06Q.OCC-.口L4234S-.Q12QSSsat.0016462.0000fifi924.62o.oao.0015151.0017773femaath..16741S5.04S4S773_45o.oai.□7235-64.2624806ma1已nonath..1546151.01S31228..44o.oao.1187133.13D516Bzi^lsath-.3297256.08405333..32o.oao.1645242.4S45271_cons1.241575.07S545315..61o.oao1.0856231.3375Z6解：(1)形式报告结果:colgpa=1.241575