tata上机实验操作

1、第六章第二题：1.建立完成的教育年数（ed）对到最近大学的距离（dist）的回归: reg ed dist, robust reg ed dist , robustlinear regressionnumber of obs = f( lz 3794)= prob > f = r-squared = root mse =379629.830.00000.00741.8074robustedcoef stderr95% conf intervalcons-.073372713.95586.0134334.0378112-5.460.000369.090.000-.099710113.881

2、72-.047035314.02999斜率估计值是:-0.0732. reg ed dist bytest female black hispanic incomehi ownhome dadcoll cue80 stwmfg80*robustlinear regression3796197.680.00000.27885425 regress ed dist bytest female black hiapanic incomehi ownhome dadcoll cue80 st;wmfg80尸robus七number of obs = f( 10r 3785)= prob > f

3、= r-squared = root mse =edcoef robuststd. err.tp>l 1195% conf.intervaldist-.0315387.0116616-2.700.007-.0544023-.0086752bytest.0938201.002980431.480.000.08797680996634female.145408.05039392.890.0040466061.2442098black.367971.06753595.450.000.23556085003812hispanic.3985196.07387635.390.000.25367855

4、433608incomehi3951984.06192076.380.000.27379725165996ownhcme1521313.06491932.340.019.02485112794115dadcoll6961324.07076029.840.000.55740068348641cue80.0232052.009312.490.013.0049521.04145833*twmfg80-.0517777.0196751-2.630.009-.0903526132029_cona8.827518.241300136.580.0008.3544279.300609dist对ed的效应估计是

5、:-0.0323. 系数下降50%,存在很大差异，（2）中回归存在遗漏变量偏差4. di e（r2_a）（可看到调整后的r2）第一问中 r2 =0.0074 调整的 r2 =0.00718796第二问中 r2 =0.2788 r2 = 0.27693235可以得到第二问中的拟合效果要优于第一问。 第二问中相似的原因：因为n很大。5. dadcoll父亲有没有念过大学：系数为正(0.6961324)衡量父亲念过大学的学生接受的教育年数平均比其父亲 没有年过大学的学生多。6.cue80county un employment rate in 1980stwmfg80state hourly wag

6、e in manufacturing in 1980.0232052-.05177771) 原因：这些参数在一定程度上构成了上大学的机会成本。2) 它们的系数估计值的符号应该如此。当stwmfgso增加时，放弃的工资增加, 所以大学入学率降低了；因而stwm馆80的系数对应为负。而当cue80增加时，人们会发现找工作很困难，这降低上大学的机会成本，所以 平均的大学入学率就会增加；因而cue80的系数对应为正。7. 带入计算即可(14.75)8. 同 7. (14.69)第七章第二题1. reg course_eval beautyobustlinear regressionnumber of

7、obs f( 1,461)prob > frsquaredroot mse=463=16.94=0.0000=0.0357=.54545course evalcoef.robuststd. err.tp>ltl95% conf.intervalbeauty.1330014.03231894.120.000.0694908.1965121_cons3.998272.0253493157.730.0003.9484584.04808795%置信区间见上表。2.linear regressionnumber of obs463f( 7,455)14.43prob > f0.0000

8、r-squared0.1556root mse.51377course evalrobustp>|t|95% conf.intervalcoef.std. err.tage-.0019545.00262180.750.456.0071068.0031978beauty.1592092.03068465.190.000.098908.2195104minority.1694282.067891-2.500.013.3028471.0360093female-.1832345.05219473.510.000-.2858071-.0806619onecredit.633.10776555.8

9、70.000.4212201.8447798intro.0079488.05654690.140.888-.1031766.1190742nnenglish-.2438402.0958959-2.540.011-.432294.0553863_cons4.16853.139034929.980.0003.89534.44176由p值得出age及intro变量均不显著。所以应该去掉。sourcessdfmsnumber of obs=463-1a 71r 31 td i i- xo. 11model21.98577025 4.39715404prob > f=0.0000residual1

10、20.25285457 .263135339r-squared=0.1546nauj k squaieatotal142.23862462 .307875801root mse=.51297courseevalcoef.std. err.tp>|t|95% conf. intervalminority-.1647853.0756893-2.180.030-.3135275-.0160431female-.1741755.049113-3.550.000-.2706909-.0776601onecredit.641325410631656.030.0004323955.8502554bea

11、uty.1660434.03062665.420.0001058569.2262299nnenglish-.24800771052349-2.360.019-.4548121-.0412033.cons4.072006.032976123.480.0004.0072034.13681变量均显著合理的置信区间应为(0 .10585690.2262299)第八章第二题：l.reg course_eval beauty intro onecredit female minority nnenglish, r reg course_eval beauty intro onecredit female

12、minority nnenglish,rlinear regressionnumber of cbs = f( 6. 456)= prob > f = r-squared = root msz =46317.03 0.0000 0.1546 .51351cour3e_evalrobustp>l t|95% confintervalcoef std. zrr zbeauty.1656103156865.250.000.10357212276478intro011325.05617410.200.840-.09906731217173onecredit6345271.10808645.

13、870.000.42211788469364female1734774.0494898-3.5100762212minority-.1666154.0674115-2.470.0341397nnenglish-24416130936345-2.610.009-42817-.0601526_cons4.068289.0370092109.930.0003.9955594.1410192. gen age2=age*age reg course_eval age age2»beauty intro onecredit female mi

14、nority nnenglish ,r gen age2=age * agereg course_eval age age2 beauty intro onecredit female minority nnenglish/rlinear regressionnumber of cbs = f( 8r 454)= prob > f = r-aquared = root mse =46312.92 0.0000 0.1573 .51383cqurae_evalcoef.robust95% conf 工n/tervwlstd. err.zp>ltlage.0195252.0234711

15、0.830.406-.02660020656507age2-.0002223.0002442-0.910.363-.0007022.0002576beauty.1596534.03064395.210.000.09943182198749intro.0024414.05644250.040.966-10847961133623onecredit.619758910859065.710.0004063564.8331614female-1881177.0517023-3.640.0865122minority-.1795689.06928820

16、.0434036nnenglish-2432153.0959732-2.530.0546085_cona3.67703254976410.0002.5966344.75743由age age2的p值可看出均大于0.05,因此不能拒绝原假设，即没有充分的 证据显示age对course_eval的效应是非线性的，也没有证据显示age对course_eval 有影响。3生成交互项：generate a= female* beauty regressco u rse_eva ifb.robust gen a=beauty* femalebeauty intro onecred

17、it female minority nnenglishreg course eval beauty a intro onecredit female minority nnenglishx rlinear regressionnumber of obs = f( 7r 45s)= prob > f = r-aquared = root mse =46315.090.00000.1639.51124course_evalrobustp>|t|95% conf. intervalcoef.std. err.beauty2308198.04778174.830.0001369195.3

18、2472a-.140741106335880.027-.2652533-.0162288intro-.0012302.0555516-0.020.982-1103998.1079393onecredit6565755.10855146.050.000.4432512.8698999female-.1729451.0493675-3.500.001-.2699617-.0759285minority-.1347426.0692342-1.950.052-.2708011.0013159nnenglish-2679069.0928796-2.880.0853808_con

19、s4.074949.0373397109.130.0004.0015694.148329female与beauty的交互作用的变量后，其p值为0.000变量是显著的，即有充 分证据表明性别不同时，beauty的效应之差存在。4. sum beautyvariableobs mean std. dev. min>max>beauty |4634.75e-08.7886477 -1.450494>1.970023可知：手术前的 beauty 为07886，术后为 0.7886,上升了 0.231 * (2 * 0.79)二 0.37.课程提高的 95%置信区间为(0.231*1.

20、96*0.048)*(2*0.79),即(0.22 , 0.51)5.计算略第八章第四题：keep if country_name != hmaltahreg growth tradeshare yearsschool 冋归 1est store m 1gen ly=ln( yearsschool)reg growth tradeshare lyest store m2gen lr=ln( rgdp60)reg growth tradeshare ly rev_coups assasinations irest store m3gen tly= tradeshare* lyreg growth

21、 tradeshare ly rev_coups assasinations lr tlyest store m4gen t2= tradeshare* tradesharegen t3= tradeshare * t2reg growth tradeshare t2 t3 ly rev_coups assasinations lrest store m5outreg2 ml m2 m3 m4 m5 using myfile , word replace see或者分步：首先drop in 651. reg growth tradeshare yearsschoolsourcessdfmsnu

22、mber of obs/ oan =64 omodel33.3764711216.6882356prob > f=0.0048residual174.431689612.85953588r-squared=0.1606一 n 1aaj kscjuareatotal207.80816633.29854222root mse=1.691growthcoef.std.err tp>ltl95% conf.intervaltradeshare1.897823.93604732.030.04702608083.769565yearsschool.2429753.0837022.900.005

23、.0756027.4103478_cons.1222363.6626687-0.180.854-1.4473241.2028522. gene ly=ln( yearsschool) reg growth tradeshare lysourcessdfmsnumber of obs =64tfi ori io onmodelresidualf l t01j ll.ll59.6761976229.8380988prob > f=0.0000148.131962612.42839283r-squared=0.2872totalaqj k scjuaxsq v z0jo207.8081663

24、3.29854222root mse= 1.5583growthcoef. std. err.t p>|t|95% conf. intervaltradeshareiy.cons1.748979.85997682.030.046.02934853.4686081.016292.22309014.560.000.57019531.462388-.185739.5642853-0.330.7431.314097.94261913. gen lr=ln( rgdp60) reg growth tradeshare ly rev_coups assasinations lrsourcessdfm

25、smodel94.1730235518.8346047residual113.635136581.95922649total207.80816633.29854222number of obs64f( 5,58)9.61prob > f0.0000r-squared0.4532adj r-squared0.4060root mse1.3997growthcoef.std. err.tp>|t|95% conf.intervaltradeshare1.10353.83315791.320.191-.56421682.771277iy2.161291.36265455.960.0001

26、.4353592.887223revcoups-2.2995371.004465-2.290.026-4.310193.2888816assasinations2277195.43365120.530.602-.64032781.095767lr-1.621135.39850464.070.000-2.418829-.8234416_cons11.745912.9198044.020.0005.90128517.590534. gen tly= tradeshare* ly reg growth tradeshare ly rev_coups assasinations lrsourcessd

27、fmsnumber of obs=64f( 6,57)8.00model94.9878003615.8313001prob > f=0.0000residual112.82036571.97930455r-squared=0.4571adj r-squared0.3999total207.80816633.29854222root mse二1.4069growthcoef.std. err.tp>ltl951 conf.intervaltradeshare1.8828071.4752921.280.2071.0714154.837029iy2.524742.67362033.750

28、.0001.1758413.873644revcoups2.350211.012683-2.320.024-4.378073.3223463assasinations.2242049.4359020.510.609.64867381.097084lr-1.641397.4017843-4.090.000-2.445956-.8368374tiy-.69008551.075573-0.640.524-2.8438831.463712cons11.498522.9599493.880.0005.5713217.425715gen t2= tradeshare* tradeshare gen t3=

29、 tradeshare* t2 regress growth tradeshare t2 t3 ly rev_coups assasinations lrsourcessdfmsmodel97.8981719713.9854531residual109.909988561.96267836total207.80816633.29854222number of obs = f( 7, 56)= prob > f = r-squared = adj r-squared = root mse =647.130.00000.47110.40501.401growthcoef.std. errtp

30、>|t|95% conf.intervaltradeshare-5.7019459.7551160.580.561-25.2437913.8399t28.48787617.435050.490.628-26.4387243.41448t3-2.7597359.249782-0.300.767-21.2892715.76981iy2.133188.36695345.810.0001.3980922.868284rev coups-2.0354541.025946-1.980.052-4.09067.0197616assasinations.1021111.44350590.230.819-

31、.7863379.9905601lr-1.584348.40794283.880.000-2.401556-.7671405_cons12.929063.0984664.170.0006.72208719.1360300 一寸-o -cm -iinyearsschool2. 预测 growth回归1预测growth的增长为0.243 x (6-4) =0.486回归 2 预测 growth 的增长为 1.016x (in6-ln4) =0.412o3. 由回归结果知；rev_coups是显著的assasinations是不显著的4. 交互项tradesharex in(yearsschool)

32、的系数不显著，所以没有证据显示一国的tradeshare 对growth的效应依赖于受教育水平。5. tradeshare2和tradeshare3的系数不显著，所以没有证据表明tradeshare和growth之间存 在非线性关系。6. 回归3预测growth的增长为1.104x (1-0.5)=0.552,冋归5预测growth的增长为5702x (1-0.5) +8.488 x (12-0.52) -2.760 x (13-0.53) =1.1-第十章第二题xtset ftps year (定义截面变量和时间变量)gen lncome=ln(income)xtreg fatality r

33、ate sb_useage speed65 speed70 bao8 drinkage21 income age, rrandom-effects gls regressionnumloer of obs=number of groups=55651group variable:f ipsr-sq:within=0.6834obs per group:min =8between=03426avg =10 9overall=0.4805max =15wald chi2(7)=1086 83corr (ui, x)=0 (assumed)prob > clii2=0 0000fatality

34、ratecoef stderrzp>|z|95% conf intervalsb_useage- 004504 0011238一 4.010 000一 0067066一.0023014speed65-0003406 0003276-1 040 298- 0009827 0003015speed700013351 00032874 060 000 0006909 0019793ba08-0013643 000367-3.720 000- 0020836一 000645drxnkage21 000767 0005097l500 132- 000232 001766income-.012615

35、4.0011453-11010 000- 0148602- 0103707age 0002318 00023940.970.333-0002373 000701_cons1379473 00891915 470 0001204664 1554282sigma_u 00301581sigma_e 0017871rho 74011223(fractionof variance dueto u_i)(1)安全带使用的系数为负，估计是显著的，安全带使用会减少死亡率。2.xtreg fatalityrate sb_useage speed65 speed70 ba08 drinkage21 income

36、 age,fe (默认地区效血)number of obsnumber of groups55651r-sq:within = between = overall =0 68680195703896obs per group: min = avg = max =810.915fixed-effects (within) regression group variable: fipscorr(u_i xb)=-0.1332f(7z498)=prob > f=156.000.0000fatalityratecoef std errtp>l t|95% confintervalsb_us

37、eage- 0057748 0011557-5.000.000-.0080455-003504speed65000425 0003339.270.204-.0010810002309speed70 0012333 00032933.740.00000058620018804ba08-.0013775.0003727-3.700.000-.00210970006452drinkage21 0007453 00050741470.142-.0002516.0017422income-0135144.0014192-9.5200107261age 0009787 00038

38、22560.01100022810017292_cons1209958 009766912 390.000.10180631401853sigma_u.00383103sigma_e 0017871rho 82128567(fractionof variance due tou_i)f test that all u_i=o:f(50, 498)=29.67prob >f = 0.0000发生了变化，因为2消除了由于时间上相同的不可观测变量所引起的遗漏变量偏差。3. xtreg fatality rate sb_useage speed65 speed70 ba08 drinkage21

39、 income age i.year, fenumk)eir of obs numloex of groups55651fixedef feats (wxtxhxn) iregiressxon group variable: fxpsr-sq:within o.7506obs per* group:mxn 8between=o1139avg =io9overall=0.0338max =15f (21z 484)=69 37corr (u_1 z xk>)=-05086prob > f=0 0000coef std errtp>l t 195% confintervalsb_

40、useage- 0037丄8600丄丄328-3280 oozl- 0059445- 0014926speed65- 000*7833 0004241-1850 065- 00：l6：l66 00005speed7 0 0008042 00034022360 0：l8 000zl358 00jl4725ba08- 000822500035丄6-2340 020- 0015134- 0001316dx>xnkaqe21- 00h337 0005353-2120 035- 0021.855- 0000819income 0062644 00386831620 106- 0013363 013

41、865ago0013丄8 00038343440 001 0005648 0020713year1984- 0004319 0011763-0370 714一 0027432 00187941985-0010707 0011803-0910 365一 0033897 00124841986- 0005777 0013086-0440 659- 003149 00199351987- 0008722 0015532-0560 575- 0039241002丄7971988- 00jl885 001751-1080 282- 0053256 00zl55561989- 0041766 001948

42、4-2140 033- 0080049- 00034821990- 005266002丄205-2480 01.3- 0094325- 00109941991一 0066623 0022348-2980 003- 0110534- 002271.11992- 008518 0024085-3540 000- 0132505- 00378551993一 0089399 0025409-3520 000- 0139324一00394751994一 0096297 0026961-3570 000- 0149273一 00433211995一 01.01123 0028675-3530 000一 0

43、157465一00447811996一 0110766 0030447-3 640 000一 01.7059-00509421997-0丄丄6075 0032097-3620 000-0丄79丄42-0053009_cons- 0779906 0382942-2040 042-153234- 00274*72sigma_u 00575372sigma_e 0016丄752rho 92675655(fractionof variance duetou_1)f test the匕 allu_i=0 :f (50,484)=3669prob >f = 0 00004. (3)更可靠，其控制了了

44、更多随时间和地区变化的变量，消除了遗漏变量偏差的影响5. 如果安全带使用率从52%上升到90%,则死亡率下降.00372x0.38=0.0014.样本屮每个周平均每一百万英里每年的死亡案例是41447.所以死亡的人数为.0014x41447=58 (人)6.xtreg sb_useage primary secondary speed65 speed70 ba08 drinkage21 income age i.year ,fefixed-effects(within) regressionnumber ofobs=556groupvariable:f ipsnumber ofgroups=5

45、1r-sq:within=08420obs por group:min=8between=0.4612avg=10.9overall=07035max=15f(22r483)=117.01corr(ui/ xb)=0.0527prob > f=0.0000sb_useagecoef stderrtp>l t|(95% confintervalprimary2055968.02813297.310.00015031892608748secondary1085184.010419810.410.000.08804481289921speed6502284860152648l500 13

46、50071450528422speed70.0120424.01205111.000.318-.01163680357215ba08003758401247470.300 76302075290282698drinkage21010714901969010.540.587-.02797390494037income058273213723990.420 67121138783279343age0138232.01360391.020 3100129070405534year1984004117504179460.100 922078004208623921985057516504211191.370.173-.0252285140261619861073522.04630882.320.02x01636051