Chapter 21 - Estimating Average Treatment Effects
Examples
------------------------------------------------------------------------------------------
name: SN
log: \iiexample21.smcl
log type: smcl
opened on: 13 May 2020, 16:33:13
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2010). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 16.1.
. * Chapter 21 - Estimating Average Treatment Effects
. ********************************************
. // Example 21.1 (Causal Effects of Job Training on Earnings, cont'd)
. * Table 21.1 Column 2 & 3
. bcuse jtrain2, clear nodesc
. eststo DiM2: reg re78 train, r
Linear regression Number of obs = 445
F(1, 443) = 7.15
Prob > F = 0.0078
R-squared = 0.0178
Root MSE = 6.5795
------------------------------------------------------------------------------
| Robust
re78 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
train | 1.794343 .6708247 2.67 0.008 .4759489 3.112737
_cons | 4.554802 .3402038 13.39 0.000 3.886188 5.223416
------------------------------------------------------------------------------
. eststo PRA2: reg re78 train age educ black hisp married re74 re75, r
Linear regression Number of obs = 445
F(8, 436) = 3.00
Prob > F = 0.0028
R-squared = 0.0548
Root MSE = 6.506
------------------------------------------------------------------------------
| Robust
re78 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
train | 1.682588 .6580774 2.56 0.011 .3891892 2.975986
age | .0557707 .0397936 1.40 0.162 -.0224405 .1339819
educ | .4058834 .1567272 2.59 0.010 .0978486 .7139182
black | -2.169781 1.008415 -2.15 0.032 -4.151741 -.1878214
hisp | .1579258 1.366293 0.12 0.908 -2.527414 2.843266
married | -.1402712 .8706551 -0.16 0.872 -1.851474 1.570932
re74 | .0828563 .1073171 0.77 0.440 -.128067 .2937795
re75 | .0515333 .1247684 0.41 0.680 -.193689 .2967557
_cons | .6217388 2.384255 0.26 0.794 -4.064324 5.307802
------------------------------------------------------------------------------
. logit train age educ black hisp married re74 re75, nolog
Logistic regression Number of obs = 445
LR chi2(7) = 8.58
Prob > chi2 = 0.2840
Log likelihood = -297.80826 Pseudo R2 = 0.0142
------------------------------------------------------------------------------
train | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | .0107155 .014017 0.76 0.445 -.0167572 .0381882
educ | .0628366 .0558026 1.13 0.260 -.0465346 .1722077
black | -.3553063 .3577202 -0.99 0.321 -1.056425 .3458123
hisp | -.9322569 .5001292 -1.86 0.062 -1.912492 .0479784
married | .1440193 .2734583 0.53 0.598 -.3919492 .6799878
re74 | -.0221324 .0252097 -0.88 0.380 -.0715425 .0272777
re75 | .0459029 .0429705 1.07 0.285 -.0383177 .1301235
_cons | -.9237055 .7693924 -1.20 0.230 -2.431687 .5842759
------------------------------------------------------------------------------
. predict yhat
(option pr assumed; Pr(train))
. g uhat = train -yhat
. g ate = uhat*re78/(yhat*(1-yhat))
. reg ate, r nohead
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.627364 .8438395 1.93 0.054 -.0310517 3.28578
------------------------------------------------------------------------------
. reg ate if yhat<=.9 & yhat>=.1, r nohead
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.627364 .8438395 1.93 0.054 -.0310517 3.28578
------------------------------------------------------------------------------
. reg ate if yhat<=.95 & yhat>=.05
Source | SS df MS Number of obs = 445
-------------+---------------------------------- F(0, 444) = 0.00
Model | 0 0 . Prob > F = .
Residual | 140689.832 444 316.86899 R-squared = 0.0000
-------------+---------------------------------- Adj R-squared = 0.0000
Total | 140689.832 444 316.86899 Root MSE = 17.801
------------------------------------------------------------------------------
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.627364 .8438395 1.93 0.054 -.0310517 3.28578
------------------------------------------------------------------------------
. foreach x of var age educ black hisp married re74 re75{
. gen u`x'= uhat*`x'
. }
. eststo PSW2: reg ate uage ueduc ublack uhisp umarried ure74 ure75, r
Linear regression Number of obs = 445
F(7, 437) = 34.42
Prob > F = 0.0000
R-squared = 0.4150
Root MSE = 13.723
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uage | .3910961 .1634717 2.39 0.017 .0698076 .7123846
ueduc | 1.734985 .4210857 4.12 0.000 .9073805 2.56259
ublack | -7.214304 3.700214 -1.95 0.052 -14.48673 .0581244
uhisp | 7.359991 8.469947 0.87 0.385 -9.286906 24.00689
umarried | -1.038132 3.765855 -0.28 0.783 -8.439571 6.363306
ure74 | .2628723 .6204462 0.42 0.672 -.9565572 1.482302
ure75 | .1593004 .5991879 0.27 0.790 -1.018348 1.336949
_cons | 1.627364 .6505422 2.50 0.013 .3487838 2.905944
------------------------------------------------------------------------------
. * Table 21.1 Column 4 & 5
. bcuse jtrain3, clear nodesc
. eststo DiM3: reg re78 train, r
Linear regression Number of obs = 2,675
F(1, 2673) = 537.36
Prob > F = 0.0000
R-squared = 0.0609
Root MSE = 15.152
------------------------------------------------------------------------------
| Robust
re78 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
train | -15.20478 .6559143 -23.18 0.000 -16.49093 -13.91863
_cons | 21.55392 .311785 69.13 0.000 20.94256 22.16529
------------------------------------------------------------------------------
. eststo PRA3: reg re78 train age educ black hisp married re74 re75, r
Linear regression Number of obs = 2,675
F(8, 2666) = 253.07
Prob > F = 0.0000
R-squared = 0.5863
Root MSE = 10.07
------------------------------------------------------------------------------
| Robust
re78 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
train | .8597703 .7665736 1.12 0.262 -.6433687 2.362909
age | -.081537 .020672 -3.94 0.000 -.1220718 -.0410022
educ | .5280233 .088394 5.97 0.000 .3546957 .701351
black | -.5427091 .4421585 -1.23 0.220 -1.409717 .3242993
hisp | 2.165568 1.218258 1.78 0.076 -.2232582 4.554394
married | 1.220271 .496305 2.46 0.014 .2470896 2.193453
re74 | .2778865 .0617851 4.50 0.000 .156735 .399038
re75 | .5681222 .0665303 8.54 0.000 .4376661 .6985784
_cons | .7767343 1.485113 0.52 0.601 -2.135356 3.688824
------------------------------------------------------------------------------
. logit train age educ black hisp married re74 re75, nolog
Logistic regression Number of obs = 2,675
LR chi2(7) = 872.82
Prob > chi2 = 0.0000
Log likelihood = -236.23799 Pseudo R2 = 0.6488
------------------------------------------------------------------------------
train | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.0840291 .014761 -5.69 0.000 -.1129601 -.055098
educ | -.0624764 .0513973 -1.22 0.224 -.1632134 .0382605
black | 2.242955 .3176941 7.06 0.000 1.620286 2.865624
hisp | 2.094338 .5584561 3.75 0.000 .9997841 3.188892
married | -1.588358 .2602448 -6.10 0.000 -2.098428 -1.078287
re74 | -.117043 .0293604 -3.99 0.000 -.1745882 -.0594977
re75 | -.2577589 .0394991 -6.53 0.000 -.3351758 -.1803421
_cons | 2.302714 .9112559 2.53 0.012 .5166853 4.088743
------------------------------------------------------------------------------
Note: 158 failures and 0 successes completely determined.
. predict yhat
(option pr assumed; Pr(train))
. g uhat = train -yhat
. g ate = uhat*re78/(yhat*(1-yhat))
. reg ate, r nohead
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 11.02854 19.17591 0.58 0.565 -26.57258 48.62966
------------------------------------------------------------------------------
. reg ate if yhat<=.9 & yhat>=.1, r nohead
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.024012 1.116107 0.92 0.360 -1.172147 3.22017
------------------------------------------------------------------------------
. reg ate if yhat<=.95 & yhat>=.05
Source | SS df MS Number of obs = 422
-------------+---------------------------------- F(0, 421) = 0.00
Model | 0 0 . Prob > F = .
Residual | 195052.966 421 463.308708 R-squared = 0.0000
-------------+---------------------------------- Adj R-squared = 0.0000
Total | 195052.966 421 463.308708 Root MSE = 21.525
------------------------------------------------------------------------------
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | -.1989809 1.047801 -0.19 0.849 -2.258555 1.860593
------------------------------------------------------------------------------
. foreach x of var age educ black hisp married re74 re75{
. gen u`x'= uhat*`x'
. }
. eststo PSW3: reg ate uage ueduc ublack uhisp umarried ure74 ure75, r
Linear regression Number of obs = 2,675
F(7, 2667) = 1.19
Prob > F = 0.3042
R-squared = 0.4822
Root MSE = 714.59
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uage | -24.32996 28.92733 -0.84 0.400 -81.05223 32.39231
ueduc | -24.78431 74.25584 -0.33 0.739 -170.3892 120.8205
ublack | -1353.41 781.8495 -1.73 0.084 -2886.503 179.6824
uhisp | -2497.521 1281.549 -1.95 0.051 -5010.452 15.40991
umarried | 1659.848 983.3841 1.69 0.092 -268.4247 3588.121
ure74 | 431.6481 333.6441 1.29 0.196 -222.5792 1085.875
ure75 | 315.1882 281.238 1.12 0.263 -236.2785 866.6548
_cons | 11.02855 13.81648 0.80 0.425 -16.06355 38.12064
------------------------------------------------------------------------------
. * Table 21.1 Column 6 & 7
. bcuse jtrain3, clear nodesc
. g rebar = (re74 + re75)/2
. keep if rebar <= 15.0
(1,513 observations deleted)
. eststo DiM3s: reg re78 train, r
Linear regression Number of obs = 1,162
F(1, 1160) = 58.05
Prob > F = 0.0000
R-squared = 0.0312
Root MSE = 10.101
------------------------------------------------------------------------------
| Robust
re78 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
train | -5.005321 .6569703 -7.62 0.000 -6.294304 -3.716338
_cons | 11.1906 .3349949 33.41 0.000 10.53334 11.84787
------------------------------------------------------------------------------
. eststo PRA3s: reg re78 train age educ black hisp married re74 re75, r
Linear regression Number of obs = 1,162
F(8, 1153) = 82.43
Prob > F = 0.0000
R-squared = 0.2797
Root MSE = 8.7365
------------------------------------------------------------------------------
| Robust
re78 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
train | 2.059039 .8011361 2.57 0.010 .4871915 3.630887
age | -.1016697 .0238413 -4.26 0.000 -.1484468 -.0548925
educ | .4046894 .099396 4.07 0.000 .2096721 .5997068
black | -1.226412 .5942647 -2.06 0.039 -2.392374 -.0604508
hisp | .2360508 .9030373 0.26 0.794 -1.53573 2.007831
married | 1.841522 .5986858 3.08 0.002 .6668861 3.016157
re74 | .2466141 .0857127 2.88 0.004 .0784438 .4147844
re75 | .6600326 .0819874 8.05 0.000 .4991713 .8208939
_cons | 2.100924 1.639151 1.28 0.200 -1.11513 5.316978
------------------------------------------------------------------------------
. logit train age educ black hisp married re74 re75, nolog
Logistic regression Number of obs = 1,162
LR chi2(7) = 601.16
Prob > chi2 = 0.0000
Log likelihood = -200.38544 Pseudo R2 = 0.6000
------------------------------------------------------------------------------
train | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.0967819 .0161755 -5.98 0.000 -.1284853 -.0650784
educ | -.0757067 .0551246 -1.37 0.170 -.1837489 .0323355
black | 2.245799 .3363927 6.68 0.000 1.586481 2.905117
hisp | 2.251116 .6025146 3.74 0.000 1.070209 3.432023
married | -1.687559 .2830233 -5.96 0.000 -2.242274 -1.132843
re74 | -.1736866 .0362111 -4.80 0.000 -.244659 -.1027142
re75 | -.3189529 .0505282 -6.31 0.000 -.4179863 -.2199194
_cons | 3.091271 .9883031 3.13 0.002 1.154233 5.02831
------------------------------------------------------------------------------
. predict yhat
(option pr assumed; Pr(train))
. g uhat = train -yhat
. g ate = uhat*re78/(yhat*(1-yhat))
. reg ate, r nohead
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 7.04867 14.69166 0.48 0.631 -21.7765 35.87384
------------------------------------------------------------------------------
. reg ate if yhat<=.9 & yhat>=.1, r nohead
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.923516 1.35118 1.42 0.156 -.7374268 4.58446
------------------------------------------------------------------------------
. reg ate if yhat<=.95 & yhat>=.05
Source | SS df MS Number of obs = 380
-------------+---------------------------------- F(0, 379) = 0.00
Model | 0 0 . Prob > F = .
Residual | 171073.277 379 451.380678 R-squared = 0.0000
-------------+---------------------------------- Adj R-squared = 0.0000
Total | 171073.277 379 451.380678 Root MSE = 21.246
------------------------------------------------------------------------------
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | .9467866 1.089883 0.87 0.386 -1.196187 3.08976
------------------------------------------------------------------------------
. foreach x of var age educ black hisp married re74 re75{
. gen u`x'= uhat*`x'
. }
. eststo PSW3s: reg ate uage ueduc ublack uhisp umarried ure74 ure75, r
Linear regression Number of obs = 1,162
F(7, 1154) = 0.38
Prob > F = 0.9171
R-squared = 0.3591
Root MSE = 402.16
------------------------------------------------------------------------------
| Robust
ate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uage | 24.64698 17.66033 1.40 0.163 -10.00298 59.29693
ueduc | -110.8254 79.11365 -1.40 0.162 -266.0481 44.39734
ublack | -446.0219 350.4072 -1.27 0.203 -1133.528 241.4845
uhisp | -1152.845 914.6663 -1.26 0.208 -2947.44 641.7507
umarried | 728.6138 533.9536 1.36 0.173 -319.0148 1776.242
ure74 | 70.85153 56.16707 1.26 0.207 -39.34948 181.0525
ure75 | 354.0008 229.487 1.54 0.123 -96.25769 804.2594
_cons | 7.04867 11.79754 0.60 0.550 -16.09836 30.1957
------------------------------------------------------------------------------
. estout DiM2 DiM3 DiM3s PRA2 PRA3 PRA3s, stats(N, fmt(%9.0g) labels("Sample size"))/*
> */ keep(train) cells(b(nostar fmt(3)) se(par fmt(3))) /*
> */ ti("Table 21.2 (Difference in means and Pooled regression adj.)")
Table 21.2 (Difference in means and Pooled regression adj.)
------------------------------------------------------------------------------------------
DiM2 DiM3 DiM3s PRA2 PRA3 PRA3s
b/se b/se b/se b/se b/se b/se
------------------------------------------------------------------------------------------
train 1.794 -15.205 -5.005 1.683 0.860 2.059
(0.671) (0.656) (0.657) (0.658) (0.767) (0.801)
------------------------------------------------------------------------------------------
Sample size 445 2675 1162 445 2675 1162
------------------------------------------------------------------------------------------
. estout PSW2 PSW3 PSW3s, stats(N, fmt(%9.0g) labels("Sample size"))/*
> */ keep(_cons) cells(b(nostar fmt(3)) se(par fmt(3))) ti("Table 21.1 (Propensity score) ")
>
Table 21.1 (Propensity score)
---------------------------------------------------
PSW2 PSW3 PSW3s
b/se b/se b/se
---------------------------------------------------
_cons 1.627 11.029 7.049
(0.651) (13.816) (11.798)
---------------------------------------------------
Sample size 445 2675 1162
---------------------------------------------------
. est clear
. // Example 21.2 (Causal Effect of Job Training on Earnings)
. bcuse jtrain2, clear nodesc
. eststo mATE2: nnmatch re78 train age educ black hisp marr re74 re75
Matching estimator: Average Treatment Effect
Weighting matrix: inverse variance Number of obs = 445
Number of matches (m) = 1
------------------------------------------------------------------------------
re78 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
SATE | 1.627792 .772682 2.11 0.035 .1133632 3.142221
------------------------------------------------------------------------------
Matching variables: age educ black hisp married re74 re75
. eststo mATT2: nnmatch re78 train age educ black hisp marr re74 re75, tc(att)
Matching estimator: Average Treatment Effect for the Treated
Weighting matrix: inverse variance Number of obs = 445
Number of matches (m) = 1
------------------------------------------------------------------------------
re78 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
SATT | 1.823749 .8819327 2.07 0.039 .0951925 3.552305
------------------------------------------------------------------------------
Matching variables: age educ black hisp married re74 re75
. bcuse jtrain3, clear nodesc
. eststo mATE3: nnmatch re78 train age educ black hisp marr re74 re75
Matching estimator: Average Treatment Effect
Weighting matrix: inverse variance Number of obs = 2675
Number of matches (m) = 1
------------------------------------------------------------------------------
re78 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
SATE | -12.86929 3.81465 -3.37 0.001 -20.34586 -5.392711
------------------------------------------------------------------------------
Matching variables: age educ black hisp married re74 re75
. eststo mATT3: nnmatch re78 train age educ black hisp marr re74 re75, tc(att)
Matching estimator: Average Treatment Effect for the Treated
Weighting matrix: inverse variance Number of obs = 2675
Number of matches (m) = 1
------------------------------------------------------------------------------
re78 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
SATT | .1548273 1.47817 0.10 0.917 -2.742333 3.051988
------------------------------------------------------------------------------
Matching variables: age educ black hisp married re74 re75
. g rebar = (re74 + re75)/2
. keep if rebar <= 15.0
(1,513 observations deleted)
. eststo mATE3s: nnmatch re78 train age educ black hisp marr re74 re75
Matching estimator: Average Treatment Effect
Weighting matrix: inverse variance Number of obs = 1162
Number of matches (m) = 1
------------------------------------------------------------------------------
re78 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
SATE | -3.846288 2.495099 -1.54 0.123 -8.736593 1.044017
------------------------------------------------------------------------------
Matching variables: age educ black hisp married re74 re75
. eststo mATT3s: nnmatch re78 train age educ black hisp marr re74 re75, tc(att)
Matching estimator: Average Treatment Effect for the Treated
Weighting matrix: inverse variance Number of obs = 1162
Number of matches (m) = 1
------------------------------------------------------------------------------
re78 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
SATT | -.2323443 1.648832 -0.14 0.888 -3.463996 2.999307
------------------------------------------------------------------------------
Matching variables: age educ black hisp married re74 re75
. estout mATE2 mATT2 mATE3 mATT3 mATE3s mATT3s, stats(N, fmt(%9.0g) labels("Sample size"))/*
> */ cells(b(nostar fmt(3)) se(par fmt(3))) ti("Table 21.2")
Table 21.2
------------------------------------------------------------------------------------------
mATE2 mATT2 mATE3 mATT3 mATE3s mATT3s
b/se b/se b/se b/se b/se b/se
------------------------------------------------------------------------------------------
SATE 1.628 -12.869 -3.846
(0.773) (3.815) (2.495)
SATT 1.824 0.155 -0.232
(0.882) (1.478) (1.649)
------------------------------------------------------------------------------------------
Sample size 445 445 2675 2675 1162 1162
------------------------------------------------------------------------------------------
. est clear
. // Example 21.3 (Estimating the Effects of Education on Fertility)
. bcuse fertil2, clear nodesc
. g w=0
. replace w=1 if educ>=7
(2,423 real changes made)
. reg children w age agesq evermarr urban electric tv
Source | SS df MS Number of obs = 4,358
-------------+---------------------------------- F(7, 4350) = 880.03
Model | 12607.4006 7 1801.05723 Prob > F = 0.0000
Residual | 8902.63153 4,350 2.04658196 R-squared = 0.5861
-------------+---------------------------------- Adj R-squared = 0.5855
Total | 21510.0321 4,357 4.93689055 Root MSE = 1.4306
------------------------------------------------------------------------------
children | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
w | -.3935524 .0495534 -7.94 0.000 -.4907024 -.2964025
age | .2719307 .0171033 15.90 0.000 .2383996 .3054618
agesq | -.001896 .0002752 -6.89 0.000 -.0024356 -.0013564
evermarr | .6947417 .0523984 13.26 0.000 .5920142 .7974691
urban | -.2437082 .0460252 -5.30 0.000 -.333941 -.1534753
electric | -.336644 .0754557 -4.46 0.000 -.4845756 -.1887124
tv | -.3259749 .0897716 -3.63 0.000 -.501973 -.1499767
_cons | -3.526605 .2451026 -14.39 0.000 -4.007131 -3.046079
------------------------------------------------------------------------------
. ivreg children (w=frsthalf) age agesq evermarr urban electric tv
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 4,358
-------------+---------------------------------- F(7, 4350) = 829.33
Model | 12154.5373 7 1736.36247 Prob > F = 0.0000
Residual | 9355.49483 4,350 2.15068847 R-squared = 0.5651
-------------+---------------------------------- Adj R-squared = 0.5644
Total | 21510.0321 4,357 4.93689055 Root MSE = 1.4665
------------------------------------------------------------------------------
children | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
w | -1.13068 .6192352 -1.83 0.068 -2.344696 .0833367
age | .2627018 .01916 13.71 0.000 .2251385 .3002651
agesq | -.0019787 .0002905 -6.81 0.000 -.0025483 -.0014091
evermarr | .6167576 .0845468 7.29 0.000 .4510028 .7825123
urban | -.1672413 .0795281 -2.10 0.036 -.3231569 -.0113257
electric | -.2343255 .1154192 -2.03 0.042 -.460606 -.0080451
tv | -.1371643 .1829146 -0.75 0.453 -.4957701 .2214415
_cons | -2.83005 .6350035 -4.46 0.000 -4.07498 -1.58512
------------------------------------------------------------------------------
Instrumented: w
Instruments: age agesq evermarr urban electric tv frsthalf
------------------------------------------------------------------------------
. probit w frsthalf age agesq evermarr urban electric tv, nolog
Probit regression Number of obs = 4,358
LR chi2(7) = 1130.84
Prob > chi2 = 0.0000
Log likelihood = -2428.384 Pseudo R2 = 0.1889
------------------------------------------------------------------------------
w | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
frsthalf | -.2206627 .0418563 -5.27 0.000 -.3026995 -.1386259
age | -.0150337 .0174845 -0.86 0.390 -.0493027 .0192354
agesq | -.0007325 .0002897 -2.53 0.011 -.0013003 -.0001647
evermarr | -.2972879 .0486734 -6.11 0.000 -.392686 -.2018898
urban | .2998122 .0432321 6.93 0.000 .2150789 .3845456
electric | .4246668 .0751255 5.65 0.000 .2774235 .57191
tv | .9281707 .0977462 9.50 0.000 .7365915 1.11975
_cons | 1.13537 .2440057 4.65 0.000 .6571273 1.613612
------------------------------------------------------------------------------
. predict what
(option pr assumed; Pr(w))
(3 missing values generated)
. ivreg children (w=what) age agesq evermarr urban electric tv
Instrumental variables (2SLS) regression
Source | SS df MS Number of obs = 4,358
-------------+---------------------------------- F(7, 4350) = 710.92
Model | 10524.2445 7 1503.46351 Prob > F = 0.0000
Residual | 10985.7876 4,350 2.52546841 R-squared = 0.4893
-------------+---------------------------------- Adj R-squared = 0.4884
Total | 21510.0321 4,357 4.93689055 Root MSE = 1.5892
------------------------------------------------------------------------------
children | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
w | -1.974509 .331779 -5.95 0.000 -2.624964 -1.324053
age | .252137 .0194358 12.97 0.000 .2140329 .290241
agesq | -.0020734 .0003079 -6.73 0.000 -.0026772 -.0014697
evermarr | .527485 .0677212 7.79 0.000 .3947169 .6602531
urban | -.0797056 .0613673 -1.30 0.194 -.2000168 .0406056
electric | -.1171961 .0953328 -1.23 0.219 -.3040969 .0697047
tv | .0789773 .1302613 0.61 0.544 -.1764013 .3343558
_cons | -2.032667 .4119708 -4.93 0.000 -2.840339 -1.224994
------------------------------------------------------------------------------
Instrumented: w
Instruments: age agesq evermarr urban electric tv what
------------------------------------------------------------------------------
. ivreg children (w=what) age agesq evermarr urban electric tv, r
Instrumental variables (2SLS) regression Number of obs = 4,358
F(7, 4350) = 678.11
Prob > F = 0.0000
R-squared = 0.4893
Root MSE = 1.5892
------------------------------------------------------------------------------
| Robust
children | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
w | -1.974509 .3135566 -6.30 0.000 -2.589239 -1.359778
age | .252137 .0210049 12.00 0.000 .2109566 .2933173
agesq | -.0020734 .0003816 -5.43 0.000 -.0028215 -.0013254
evermarr | .527485 .0695789 7.58 0.000 .391075 .663895
urban | -.0797056 .0605259 -1.32 0.188 -.1983672 .038956
electric | -.1171961 .0891859 -1.31 0.189 -.2920458 .0576536
tv | .0789773 .1084846 0.73 0.467 -.1337078 .2916623
_cons | -2.032667 .3642986 -5.58 0.000 -2.746877 -1.318456
------------------------------------------------------------------------------
Instrumented: w
Instruments: age agesq evermarr urban electric tv what
------------------------------------------------------------------------------
. log close
name: SN
log: iiexample21.smcl
log type: smcl
closed on: 13 May 2020, 16:33:52
------------------------------------------------------------------------------------------