------------------------------------------------------------------------------------- name: log: /Users/rx59economics.rutgers.edu/Library/CloudStorage/Box-Box/322/Spring > 2025/Problem Sets/PS5/PS5_Stata.log log type: text opened on: 20 Apr 2025, 17:46:32 . . clear all . . // Q1 . * load data for Q1 . use beauty . . gen femaleXbigcity = female*bigcity . reg wage female if bigcity==1, r Linear regression Number of obs = 276 F(1, 274) = 10.37 Prob > F = 0.0014 R-squared = 0.0506 Root MSE = 6.0297 ------------------------------------------------------------------------------ | Robust wage | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- female | -2.966226 .92119 -3.22 0.001 -4.779736 -1.152716 _cons | 8.763529 .3499681 25.04 0.000 8.074561 9.452497 ------------------------------------------------------------------------------ . reg wage female if bigcity==0, r Linear regression Number of obs = 984 F(1, 982) = 218.71 Prob > F = 0.0000 R-squared = 0.1296 Root MSE = 3.7728 ------------------------------------------------------------------------------ | Robust wage | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- female | -3.044229 .2058482 -14.79 0.000 -3.448182 -2.640276 _cons | 6.959388 .1766444 39.40 0.000 6.612744 7.306032 ------------------------------------------------------------------------------ . reg wage female bigcity femaleXbigcity, r Linear regression Number of obs = 1,260 F(3, 1256) = 115.03 Prob > F = 0.0000 R-squared = 0.1246 Root MSE = 4.3658 -------------------------------------------------------------------------------- | Robust wage | Coefficient std. err. t P>|t| [95% conf. interval] ---------------+---------------------------------------------------------------- female | -3.044229 .2059661 -14.78 0.000 -3.448305 -2.640154 bigcity | 1.804142 .3914286 4.61 0.000 1.036216 2.572068 femaleXbigcity | .0780032 .942097 0.08 0.934 -1.770254 1.926261 _cons | 6.959388 .1767456 39.38 0.000 6.612639 7.306137 -------------------------------------------------------------------------------- . . test female femaleXbigcity ( 1) female = 0 ( 2) femaleXbigcity = 0 F( 2, 1256) = 114.43 Prob > F = 0.0000 . . // Q2 . * load data for Q2 . clear . use loanapp . gen obratXblack = obrat*black . gen marriedXblack = married*black (3 missing values generated) . reg approve obrat married black unem obratXblack marriedXblack, r Linear regression Number of obs = 1,986 F(6, 1979) = 14.58 Prob > F = 0.0000 R-squared = 0.0728 Root MSE = .31665 ------------------------------------------------------------------------------- | Robust approve | Coefficient std. err. t P>|t| [95% conf. interval] --------------+---------------------------------------------------------------- obrat | -.0057637 .0011382 -5.06 0.000 -.0079959 -.0035315 married | .0363469 .0154948 2.35 0.019 .0059591 .0667347 black | -.0515167 .161999 -0.32 0.751 -.3692232 .2661898 unem | -.0093678 .0038155 -2.46 0.014 -.0168506 -.0018849 obratXblack | -.0041854 .0043397 -0.96 0.335 -.0126962 .0043255 marriedXblack | -.017235 .069682 -0.25 0.805 -.1538929 .1194228 _cons | 1.096972 .0392555 27.94 0.000 1.019986 1.173959 ------------------------------------------------------------------------------- . lincom _cons + married + unem*3 ( 1) married + 3*unem + _cons = 0 ------------------------------------------------------------------------------ approve | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- (1) | 1.105216 .0363902 30.37 0.000 1.033849 1.176583 ------------------------------------------------------------------------------ . test black obratXblack marriedXblack ( 1) black = 0 ( 2) obratXblack = 0 ( 3) marriedXblack = 0 F( 3, 1979) = 12.53 Prob > F = 0.0000 . . // Q3 . * load data for Q3 . use Fertil2, clear . reg children educ age agesq tv, robust Linear regression Number of obs = 4,359 F(4, 4354) = 1443.36 Prob > F = 0.0000 R-squared = 0.5707 Root MSE = 1.4564 ------------------------------------------------------------------------------ | Robust children | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- educ | -.0805499 .0063355 -12.71 0.000 -.0929707 -.0681292 age | .3349376 .019191 17.45 0.000 .2973134 .3725618 agesq | -.0026403 .0003517 -7.51 0.000 -.0033299 -.0019507 tv | -.38201 .0695883 -5.49 0.000 -.5184386 -.2455815 _cons | -4.222158 .2436288 -17.33 0.000 -4.699794 -3.744522 ------------------------------------------------------------------------------ . lincom _cons + 8*educ + 30*age + 900*agesq + 1*tv ( 1) 8*educ + 30*age + 900*agesq + tv + _cons = 0 ------------------------------------------------------------------------------ children | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- (1) | 2.423279 .0668954 36.22 0.000 2.29213 2.554428 ------------------------------------------------------------------------------ . . reg educ frsthalf age agesq tv, robust Linear regression Number of obs = 4,359 F(4, 4354) = 292.26 Prob > F = 0.0000 R-squared = 0.2058 Root MSE = 3.5011 ------------------------------------------------------------------------------ | Robust educ | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- frsthalf | -.7296708 .1069749 -6.82 0.000 -.9393961 -.5199455 age | -.1239705 .0373336 -3.32 0.001 -.1971635 -.0507776 agesq | -.0003436 .0006236 -0.55 0.582 -.0015662 .000879 tv | 4.25101 .221413 19.20 0.000 3.816928 4.685092 _cons | 9.537741 .5083612 18.76 0.000 8.541094 10.53439 ------------------------------------------------------------------------------ . . ivregress 2sls children age agesq tv (educ=frsthalf), robust Instrumental-variables 2SLS regression Number of obs = 4,359 Wald chi2(4) = 5507.90 Prob > chi2 = 0.0000 R-squared = 0.5495 Root MSE = 1.4911 ------------------------------------------------------------------------------ | Robust children | Coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- educ | -.1725337 .0612737 -2.82 0.005 -.292628 -.0524394 age | .3234057 .0208731 15.49 0.000 .2824952 .3643162 agesq | -.0026722 .000352 -7.59 0.000 -.003362 -.0019824 tv | .0148507 .2715859 0.05 0.956 -.5174479 .5471493 _cons | -3.377884 .614269 -5.50 0.000 -4.581829 -2.173939 ------------------------------------------------------------------------------ Endogenous: educ Exogenous: age agesq tv frsthalf . . ivregress 2sls children age agesq tv radio electric bicycle (educ=frsthalf), /// > robust Instrumental-variables 2SLS regression Number of obs = 4,355 Wald chi2(7) = 5623.98 Prob > chi2 = 0.0000 R-squared = 0.5554 Root MSE = 1.4814 ------------------------------------------------------------------------------ | Robust children | Coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- educ | -.1715124 .0695915 -2.46 0.014 -.3079092 -.0351156 age | .3263289 .0218512 14.93 0.000 .2835013 .3691565 agesq | -.0027019 .00035 -7.72 0.000 -.003388 -.0020159 tv | -.0172654 .195562 -0.09 0.930 -.4005598 .366029 radio | .1715746 .129277 1.33 0.184 -.0818037 .424953 electric | -.1002708 .1622123 -0.62 0.536 -.4182011 .2176596 bicycle | .3026615 .0508344 5.95 0.000 .2030279 .4022951 _cons | -3.625946 .6181138 -5.87 0.000 -4.837427 -2.414465 ------------------------------------------------------------------------------ Endogenous: educ Exogenous: age agesq tv radio electric bicycle frsthalf . . log close name: log: /Users/rx59economics.rutgers.edu/Library/CloudStorage/Box-Box/322/Spring > 2025/Problem Sets/PS5/PS5_Stata.log log type: text closed on: 20 Apr 2025, 17:46:37 -------------------------------------------------------------------------------------