This web page contains the log file from the example imputation discussed in the Imputing section, plus the graphics it creates.
----------------------------------------------------------------------------------------------------------------------------------
name:
log: \sscc\pubs\mi\miex.log
log type: text
opened on: 17 Aug 2012, 10:51:48
.
. use midata
.
. // test missingness of data
. unab numvars: *
. unab missvars: urban-wage
. misstable sum, gen(miss_)
Obs<.
+------------------------------
| | Unique
Variable | Obs=. Obs>. Obs<. | values Min Max
-------------+--------------------------------+------------------------------
race | 293 2,707 | 3 0 2
urban | 273 2,727 | 2 0 1
edu | 319 2,681 | 4 1 4
exp | 293 2,707 | >500 0 47.8623
wage | 299 2,701 | >500 0 227465.2
-----------------------------------------------------------------------------
.
. foreach var of local missvars {
2. local covars: list numvars - var
3. display _newline(3) "logit missingness of `var' on `covars'"
4. logit miss_`var' `covars'
5. foreach nvar of local covars {
6. display _newline(3) "ttest of `nvar' by missingness of `var'"
7. ttest `nvar', by(miss_`var')
8. }
9. }
logit missingness of urban on female race edu exp wage
Iteration 0: log likelihood = -613.04047
Iteration 1: log likelihood = -611.32144
Iteration 2: log likelihood = -611.31554
Iteration 3: log likelihood = -611.31554
Logistic regression Number of obs = 1964
LR chi2(5) = 3.45
Prob > chi2 = 0.6310
Log likelihood = -611.31554 Pseudo R2 = 0.0028
------------------------------------------------------------------------------
miss_urban | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | .1505333 .1696945 0.89 0.375 -.1820618 .4831284
race | -.068621 .0980029 -0.70 0.484 -.260703 .1234611
edu | .0098348 .0973647 0.10 0.920 -.1809964 .200666
exp | -.0033092 .0094184 -0.35 0.725 -.0217689 .0151504
wage | 3.68e-06 2.57e-06 1.43 0.153 -1.36e-06 8.71e-06
_cons | -2.513739 .2871859 -8.75 0.000 -3.076613 -1.950865
------------------------------------------------------------------------------
ttest of female by missingness of urban
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2727 .4979831 .0095764 .5000876 .4792053 .5167609
1 | 273 .4761905 .0302826 .50035 .4165725 .5358085
---------+--------------------------------------------------------------------
combined | 3000 .496 .0091299 .5000674 .4780984 .5139016
---------+--------------------------------------------------------------------
diff | .0217927 .0317471 -.0404556 .0840409
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.6864
Ho: diff = 0 degrees of freedom = 2998
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.7538 Pr(|T| > |t|) = 0.4925 Pr(T > t) = 0.2462
ttest of race by missingness of urban
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2456 1.014658 .0163483 .8101878 .9826002 1.046716
1 | 251 1.055777 .0513125 .8129431 .954717 1.156837
---------+--------------------------------------------------------------------
combined | 2707 1.018471 .0155756 .8103808 .9879293 1.049012
---------+--------------------------------------------------------------------
diff | -.0411189 .0537051 -.1464261 .0641883
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.7656
Ho: diff = 0 degrees of freedom = 2705
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.2220 Pr(|T| > |t|) = 0.4440 Pr(T > t) = 0.7780
ttest of edu by missingness of urban
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2442 2.356675 .0184465 .9115617 2.320503 2.392847
1 | 239 2.368201 .0595328 .9203542 2.250922 2.485479
---------+--------------------------------------------------------------------
combined | 2681 2.357702 .017617 .912182 2.323158 2.392247
---------+--------------------------------------------------------------------
diff | -.011526 .0618353 -.1327757 .1097237
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.1864
Ho: diff = 0 degrees of freedom = 2679
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.4261 Pr(|T| > |t|) = 0.8521 Pr(T > t) = 0.5739
ttest of exp by missingness of urban
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2450 15.56019 .1954164 9.67262 15.17699 15.94339
1 | 257 15.69341 .5938361 9.519917 14.52399 16.86284
---------+--------------------------------------------------------------------
combined | 2707 15.57284 .1856003 9.656566 15.20891 15.93677
---------+--------------------------------------------------------------------
diff | -.1332234 .6332773 -1.37498 1.108533
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.2104
Ho: diff = 0 degrees of freedom = 2705
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.4167 Pr(|T| > |t|) = 0.8334 Pr(T > t) = 0.5833
ttest of wage by missingness of urban
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2458 71240.46 763.6437 37860.09 69743.01 72737.91
1 | 243 74058.12 2597.11 40484.95 68942.29 79173.95
---------+--------------------------------------------------------------------
combined | 2701 71493.95 733.1819 38104.3 70056.3 72931.61
---------+--------------------------------------------------------------------
diff | -2817.665 2562.273 -7841.881 2206.551
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -1.0997
Ho: diff = 0 degrees of freedom = 2699
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.1358 Pr(|T| > |t|) = 0.2716 Pr(T > t) = 0.8642
logit missingness of edu on female race urban exp wage
Iteration 0: log likelihood = -670.64062
Iteration 1: log likelihood = -669.91049
Iteration 2: log likelihood = -669.90956
Iteration 3: log likelihood = -669.90956
Logistic regression Number of obs = 1989
LR chi2(5) = 1.46
Prob > chi2 = 0.9174
Log likelihood = -669.90956 Pseudo R2 = 0.0011
------------------------------------------------------------------------------
miss_edu | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | -.0194159 .1557151 -0.12 0.901 -.3246119 .2857801
race | .0569055 .0903871 0.63 0.529 -.1202499 .2340609
urban | .0476788 .157765 0.30 0.762 -.2615349 .3568925
exp | -.0028472 .0086668 -0.33 0.743 -.0198338 .0141393
wage | 1.93e-06 2.25e-06 0.86 0.390 -2.47e-06 6.33e-06
_cons | -2.314849 .2528625 -9.15 0.000 -2.81045 -1.819248
------------------------------------------------------------------------------
ttest of female by missingness of edu
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2681 .4983215 .0096583 .5000905 .4793831 .51726
1 | 319 .476489 .0280076 .5002316 .4213854 .5315926
---------+--------------------------------------------------------------------
combined | 3000 .496 .0091299 .5000674 .4780984 .5139016
---------+--------------------------------------------------------------------
diff | .0218325 .0296195 -.0362442 .0799092
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.7371
Ho: diff = 0 degrees of freedom = 2998
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.7694 Pr(|T| > |t|) = 0.4611 Pr(T > t) = 0.2306
ttest of race by missingness of edu
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2416 1.016142 .0164994 .8109934 .9837879 1.048497
1 | 291 1.037801 .0472723 .8064058 .9447603 1.130841
---------+--------------------------------------------------------------------
combined | 2707 1.018471 .0155756 .8103808 .9879293 1.049012
---------+--------------------------------------------------------------------
diff | -.0216583 .0502926 -.120274 .0769574
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.4306
Ho: diff = 0 degrees of freedom = 2705
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.3334 Pr(|T| > |t|) = 0.6668 Pr(T > t) = 0.6666
ttest of urban by missingness of edu
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2442 .6588862 .0095956 .4741806 .6400698 .6777025
1 | 285 .6912281 .0274139 .4627995 .6372679 .7451882
---------+--------------------------------------------------------------------
combined | 2727 .6622662 .0090582 .473024 .6445046 .6800278
---------+--------------------------------------------------------------------
diff | -.0323419 .0296084 -.0903991 .0257153
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -1.0923
Ho: diff = 0 degrees of freedom = 2725
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.1374 Pr(|T| > |t|) = 0.2748 Pr(T > t) = 0.8626
ttest of exp by missingness of edu
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2419 15.61121 .1972722 9.702505 15.22437 15.99805
1 | 288 15.25056 .5463414 9.27172 14.17522 16.32591
---------+--------------------------------------------------------------------
combined | 2707 15.57284 .1856003 9.656566 15.20891 15.93677
---------+--------------------------------------------------------------------
diff | .3606459 .6020106 -.8198013 1.541093
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.5991
Ho: diff = 0 degrees of freedom = 2705
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.7254 Pr(|T| > |t|) = 0.5492 Pr(T > t) = 0.2746
ttest of wage by missingness of edu
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2412 71484.16 778.2065 38219.37 69958.14 73010.19
1 | 289 71575.65 2187.928 37194.77 67269.3 75882.01
---------+--------------------------------------------------------------------
combined | 2701 71493.95 733.1819 38104.3 70056.3 72931.61
---------+--------------------------------------------------------------------
diff | -91.48891 2372.352 -4743.299 4560.321
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.0386
Ho: diff = 0 degrees of freedom = 2699
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.4846 Pr(|T| > |t|) = 0.9692 Pr(T > t) = 0.5154
logit missingness of exp on female race urban edu wage
Iteration 0: log likelihood = -654.79701
Iteration 1: log likelihood = -653.43555
Iteration 2: log likelihood = -653.43222
Iteration 3: log likelihood = -653.43222
Logistic regression Number of obs = 1982
LR chi2(5) = 2.73
Prob > chi2 = 0.7416
Log likelihood = -653.43222 Pseudo R2 = 0.0021
------------------------------------------------------------------------------
miss_exp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | .0225336 .1628237 0.14 0.890 -.296595 .3416622
race | -.0595427 .0946498 -0.63 0.529 -.2450529 .1259675
urban | .0187602 .1634337 0.11 0.909 -.301564 .3390845
edu | .1058189 .0930734 1.14 0.256 -.0766016 .2882394
wage | -2.42e-06 2.20e-06 -1.10 0.271 -6.73e-06 1.89e-06
_cons | -2.216882 .2563097 -8.65 0.000 -2.71924 -1.714524
------------------------------------------------------------------------------
ttest of female by missingness of exp
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2707 .4931659 .0096109 .5000457 .4743204 .5120114
1 | 293 .5221843 .0292315 .5003622 .4646532 .5797154
---------+--------------------------------------------------------------------
combined | 3000 .496 .0091299 .5000674 .4780984 .5139016
---------+--------------------------------------------------------------------
diff | -.0290184 .0307552 -.0893219 .0312851
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.9435
Ho: diff = 0 degrees of freedom = 2998
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.1727 Pr(|T| > |t|) = 0.3455 Pr(T > t) = 0.8273
ttest of race by missingness of exp
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2448 1.020425 .0163788 .8103788 .9883071 1.052543
1 | 259 1 .0504388 .8117356 .9006758 1.099324
---------+--------------------------------------------------------------------
combined | 2707 1.018471 .0155756 .8103808 .9879293 1.049012
---------+--------------------------------------------------------------------
diff | .0204248 .0529598 -.0834209 .1242705
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.3857
Ho: diff = 0 degrees of freedom = 2705
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.6501 Pr(|T| > |t|) = 0.6998 Pr(T > t) = 0.3499
ttest of urban by missingness of exp
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2450 .6628571 .0095526 .4728306 .6441251 .6815892
1 | 277 .6570397 .0285735 .4755575 .6007901 .7132894
---------+--------------------------------------------------------------------
combined | 2727 .6622662 .0090582 .473024 .6445046 .6800278
---------+--------------------------------------------------------------------
diff | .0058174 .0299902 -.0529884 .0646233
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.1940
Ho: diff = 0 degrees of freedom = 2725
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.5769 Pr(|T| > |t|) = 0.8462 Pr(T > t) = 0.4231
ttest of edu by missingness of exp
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2419 2.355105 .0185189 .9108219 2.318791 2.39142
1 | 262 2.381679 .0572124 .9260638 2.269023 2.494336
---------+--------------------------------------------------------------------
combined | 2681 2.357702 .017617 .912182 2.323158 2.392247
---------+--------------------------------------------------------------------
diff | -.026574 .0593371 -.1429251 .0897771
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.4478
Ho: diff = 0 degrees of freedom = 2679
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.3271 Pr(|T| > |t|) = 0.6543 Pr(T > t) = 0.6729
ttest of wage by missingness of exp
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2432 71682.77 773.3145 38136.25 70166.35 73199.2
1 | 269 69786.84 2307.266 37841.97 65244.17 74329.51
---------+--------------------------------------------------------------------
combined | 2701 71493.95 733.1819 38104.3 70056.3 72931.61
---------+--------------------------------------------------------------------
diff | 1895.932 2448.559 -2905.309 6697.173
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.7743
Ho: diff = 0 degrees of freedom = 2699
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.7806 Pr(|T| > |t|) = 0.4388 Pr(T > t) = 0.2194
logit missingness of wage on female race urban edu exp
Iteration 0: log likelihood = -647.94103
Iteration 1: log likelihood = -645.05158
Iteration 2: log likelihood = -645.0361
Iteration 3: log likelihood = -645.0361
Logistic regression Number of obs = 1979
LR chi2(5) = 5.81
Prob > chi2 = 0.3252
Log likelihood = -645.0361 Pseudo R2 = 0.0045
------------------------------------------------------------------------------
miss_wage | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | -.191566 .1570953 -1.22 0.223 -.4994672 .1163353
race | -.1705262 .0959515 -1.78 0.076 -.3585876 .0175352
urban | -.1708259 .1599631 -1.07 0.286 -.4843478 .142696
edu | .0710834 .0886472 0.80 0.423 -.102662 .2448288
exp | .0040734 .0079491 0.51 0.608 -.0115065 .0196534
_cons | -2.049828 .2771956 -7.39 0.000 -2.593121 -1.506535
------------------------------------------------------------------------------
ttest of female by missingness of wage
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2701 .5012958 .0096225 .5000909 .4824277 .520164
1 | 299 .4481605 .0288081 .4981391 .3914674 .5048537
---------+--------------------------------------------------------------------
combined | 3000 .496 .0091299 .5000674 .4780984 .5139016
---------+--------------------------------------------------------------------
diff | .0531353 .030468 -.006605 .1128755
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 1.7440
Ho: diff = 0 degrees of freedom = 2998
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.9594 Pr(|T| > |t|) = 0.0813 Pr(T > t) = 0.0406
ttest of race by missingness of wage
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2442 1.020885 .0164342 .8121201 .9886582 1.053111
1 | 265 .9962264 .0488572 .7953373 .9000271 1.092426
---------+--------------------------------------------------------------------
combined | 2707 1.018471 .0155756 .8103808 .9879293 1.049012
---------+--------------------------------------------------------------------
diff | .0246581 .0524204 -.0781299 .1274461
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.4704
Ho: diff = 0 degrees of freedom = 2705
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.6809 Pr(|T| > |t|) = 0.6381 Pr(T > t) = 0.3191
ttest of urban by missingness of wage
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2458 .6647681 .0095237 .4721675 .6460928 .6834434
1 | 269 .6394052 .0293312 .4810681 .5816562 .6971542
---------+--------------------------------------------------------------------
combined | 2727 .6622662 .0090582 .473024 .6445046 .6800278
---------+--------------------------------------------------------------------
diff | .0253629 .0303797 -.0342066 .0849324
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.8349
Ho: diff = 0 degrees of freedom = 2725
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.7981 Pr(|T| > |t|) = 0.4039 Pr(T > t) = 0.2019
ttest of edu by missingness of wage
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2412 2.357794 .0185831 .9126566 2.321354 2.394235
1 | 269 2.356877 .0554598 .9096083 2.247685 2.46607
---------+--------------------------------------------------------------------
combined | 2681 2.357702 .017617 .912182 2.323158 2.392247
---------+--------------------------------------------------------------------
diff | .000917 .058647 -.114081 .1159151
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 0.0156
Ho: diff = 0 degrees of freedom = 2679
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.5062 Pr(|T| > |t|) = 0.9875 Pr(T > t) = 0.4938
ttest of exp by missingness of wage
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 2432 15.51836 .1952193 9.627299 15.13555 15.90117
1 | 275 16.05461 .5979892 9.916529 14.87737 17.23184
---------+--------------------------------------------------------------------
combined | 2707 15.57284 .1856003 9.656566 15.20891 15.93677
---------+--------------------------------------------------------------------
diff | -.5362457 .6143811 -1.74095 .6684581
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.8728
Ho: diff = 0 degrees of freedom = 2705
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.1914 Pr(|T| > |t|) = 0.3828 Pr(T > t) = 0.8086
.
.
. // set up trial imputation command just to get the individual regression commands
. mi set wide
. mi register imputed race-wage
. mi register regular female
. mi impute chained (logit) urban (mlogit) race (ologit) edu (regress) exp wage = i.female, dryrun
Conditional models:
urban: logit urban i.race exp wage i.edu i.female
race: mlogit race i.urban exp wage i.edu i.female
exp: regress exp i.urban i.race wage i.edu i.female
wage: regress wage i.urban i.race exp i.edu i.female
edu: ologit edu i.urban i.race exp wage i.female
.
. // test imputation model for race
. mlogit race exp wage i.edu i.urban i.female
Iteration 0: log likelihood = -1953.43
Iteration 1: log likelihood = -1879.0566
Iteration 2: log likelihood = -1877.6678
Iteration 3: log likelihood = -1877.6668
Iteration 4: log likelihood = -1877.6668
Multinomial logistic regression Number of obs = 1779
LR chi2(14) = 151.53
Prob > chi2 = 0.0000
Log likelihood = -1877.6668 Pseudo R2 = 0.0388
------------------------------------------------------------------------------
race | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
0 |
exp | -.0160644 .0075448 -2.13 0.033 -.030852 -.0012767
wage | 5.80e-06 2.07e-06 2.80 0.005 1.75e-06 9.85e-06
|
edu |
2 | -.8129621 .1829418 -4.44 0.000 -1.171521 -.4544027
3 | -1.593897 .1971316 -8.09 0.000 -1.980268 -1.207526
4 | -2.72232 .2886243 -9.43 0.000 -3.288013 -2.156626
|
1.urban | .7865707 .1339259 5.87 0.000 .5240808 1.049061
1.female | .2893221 .1342653 2.15 0.031 .026167 .5524772
_cons | .226939 .225613 1.01 0.314 -.2152544 .6691324
-------------+----------------------------------------------------------------
1 |
exp | .0052958 .0071395 0.74 0.458 -.0086974 .0192891
wage | 6.69e-07 1.97e-06 0.34 0.734 -3.19e-06 4.53e-06
|
edu |
2 | -.5144888 .1832349 -2.81 0.005 -.8736226 -.155355
3 | -1.125629 .1949919 -5.77 0.000 -1.507806 -.743452
4 | -1.307677 .2464598 -5.31 0.000 -1.790729 -.8246246
|
1.urban | .4772458 .1266699 3.77 0.000 .2289775 .7255142
1.female | .1276518 .1290494 0.99 0.323 -.1252803 .380584
_cons | .253432 .220844 1.15 0.251 -.1794143 .6862783
-------------+----------------------------------------------------------------
2 | (base outcome)
------------------------------------------------------------------------------
. // test for misspecification by adding interactions
. mlogit race (c.exp c.wage i.edu)##(i.female i.urban)
Iteration 0: log likelihood = -1953.43
Iteration 1: log likelihood = -1873.2138
Iteration 2: log likelihood = -1871.3005
Iteration 3: log likelihood = -1871.2474
Iteration 4: log likelihood = -1871.2473
Multinomial logistic regression Number of obs = 1779
LR chi2(34) = 164.37
Prob > chi2 = 0.0000
Log likelihood = -1871.2473 Pseudo R2 = 0.0421
-------------------------------------------------------------------------------
race | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------+----------------------------------------------------------------
0 |
exp | -.0032793 .0152468 -0.22 0.830 -.0331624 .0266038
wage | 7.15e-06 4.01e-06 1.78 0.075 -7.13e-07 .000015
|
edu |
2 | -.7354223 .3525541 -2.09 0.037 -1.426416 -.044429
3 | -1.842053 .4005341 -4.60 0.000 -2.627085 -1.05702
4 | -3.830948 1.095232 -3.50 0.000 -5.977563 -1.684332
|
1.female | .1884419 .4158562 0.45 0.650 -.6266212 1.003505
1.urban | 1.056967 .4258448 2.48 0.013 .2223265 1.891608
|
female#c.exp |
1 | -.0064319 .0151943 -0.42 0.672 -.0362122 .0233484
|
urban#c.exp |
1 | -.0133441 .0161867 -0.82 0.410 -.0450693 .0183812
|
female#c.wage |
1 | 1.91e-06 4.18e-06 0.46 0.648 -6.29e-06 .0000101
|
urban#c.wage |
1 | -3.07e-06 4.27e-06 -0.72 0.472 -.0000114 5.30e-06
|
edu#female |
2 1 | -.0181898 .3925029 -0.05 0.963 -.7874813 .7511017
3 1 | -.0172244 .4175976 -0.04 0.967 -.8357006 .8012518
4 1 | .4977631 .6962046 0.71 0.475 -.8667728 1.862299
|
edu#urban |
2 1 | -.0763182 .3951518 -0.19 0.847 -.8508015 .6981651
3 1 | .3914775 .440372 0.89 0.374 -.4716358 1.254591
4 1 | .8667237 1.155459 0.75 0.453 -1.397934 3.131381
|
_cons | .0469868 .3959872 0.12 0.906 -.7291337 .8231074
--------------+----------------------------------------------------------------
1 |
exp | .0117503 .0139703 0.84 0.400 -.015631 .0391316
wage | 6.92e-07 3.72e-06 0.19 0.852 -6.59e-06 7.98e-06
|
edu |
2 | -.4485059 .3458792 -1.30 0.195 -1.126417 .2294049
3 | -1.31316 .3798982 -3.46 0.001 -2.057747 -.5685735
4 | -1.904266 .5852199 -3.25 0.001 -3.051275 -.7572556
|
1.female | .1574212 .4125146 0.38 0.703 -.6510925 .9659349
1.urban | .3765925 .421229 0.89 0.371 -.4490011 1.202186
|
female#c.exp |
1 | -.0173227 .0144194 -1.20 0.230 -.0455843 .0109389
|
urban#c.exp |
1 | .0034701 .015149 0.23 0.819 -.0262214 .0331615
|
female#c.wage |
1 | 3.64e-06 4.00e-06 0.91 0.363 -4.20e-06 .0000115
|
urban#c.wage |
1 | -2.70e-06 4.03e-06 -0.67 0.503 -.0000106 5.20e-06
|
edu#female |
2 1 | -.227974 .3945182 -0.58 0.563 -1.001215 .5452674
3 1 | -.0181844 .414751 -0.04 0.965 -.8310815 .7947127
4 1 | .4709082 .5673412 0.83 0.407 -.6410601 1.582876
|
edu#urban |
2 1 | .0936678 .3968078 0.24 0.813 -.6840612 .8713968
3 1 | .3483946 .4271216 0.82 0.415 -.4887483 1.185538
4 1 | .3778603 .6590117 0.57 0.566 -.913779 1.6695
|
_cons | .2636095 .3807272 0.69 0.489 -.4826021 1.009821
--------------+----------------------------------------------------------------
2 | (base outcome)
-------------------------------------------------------------------------------
.
. // test imputation model for exp
. regress exp i.race wage i.edu i.urban i.female
Source | SS df MS Number of obs = 1779
-------------+------------------------------ F( 8, 1770) = 93.75
Model | 49906.8412 8 6238.35514 Prob > F = 0.0000
Residual | 117780.77 1770 66.5428078 R-squared = 0.2976
-------------+------------------------------ Adj R-squared = 0.2944
Total | 167687.611 1778 94.3124921 Root MSE = 8.1574
------------------------------------------------------------------------------
exp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
race |
1 | 1.391616 .4799807 2.90 0.004 .450227 2.333004
2 | 1.031061 .4947771 2.08 0.037 .0606522 2.00147
|
wage | .0001343 5.71e-06 23.50 0.000 .0001231 .0001455
|
edu |
2 | -1.96545 .5537782 -3.55 0.000 -3.051578 -.8793225
3 | -5.058849 .5979374 -8.46 0.000 -6.231586 -3.886111
4 | -7.905853 .8106653 -9.75 0.000 -9.495815 -6.315891
|
1.urban | -.5730682 .4234363 -1.35 0.176 -1.403556 .2574196
1.female | -1.111798 .4256352 -2.61 0.009 -1.946598 -.2769972
_cons | 9.243531 .716064 12.91 0.000 7.839111 10.64795
------------------------------------------------------------------------------
. // test for misspecification with rvfplot
. // constraint line indicates exp>=0
. rvfplot, ylabel(-40 -20 0 20 40)
. graph export exp1.png, replace
(file exp1.png written in PNG format)
. predict exphat
(option xb assumed; fitted values)
(1018 missing values generated)
. predict expres, res
(1221 missing values generated)
. gen y=-exphat
(1018 missing values generated)
. scatter expres exphat || line y exphat, legend(order(2 "Exp>=0 Constraint"))
. graph export exp2.png, replace
(file exp2.png written in PNG format)
. drop expres exphat y
. //test for misspecification by adding interactions
. regress exp (i.race i.urban i.female)##(c.wage i.edu)
Source | SS df MS Number of obs = 1779
-------------+------------------------------ F( 24, 1754) = 32.28
Model | 51376.4689 24 2140.6862 Prob > F = 0.0000
Residual | 116311.142 1754 66.3119396 R-squared = 0.3064
-------------+------------------------------ Adj R-squared = 0.2969
Total | 167687.611 1778 94.3124921 Root MSE = 8.1432
-------------------------------------------------------------------------------
exp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------+----------------------------------------------------------------
race |
1 | .8903704 1.271895 0.70 0.484 -1.60422 3.384961
2 | 1.418534 1.46223 0.97 0.332 -1.449363 4.286431
|
1.urban | -1.905077 1.19761 -1.59 0.112 -4.25397 .4438155
1.female | -.0698405 1.188445 -0.06 0.953 -2.400758 2.261077
wage | .0001473 .0000136 10.87 0.000 .0001207 .0001739
|
edu |
2 | -3.806439 1.314344 -2.90 0.004 -6.384285 -1.228592
3 | -6.196198 1.456027 -4.26 0.000 -9.05193 -3.340466
4 | -8.003504 2.559556 -3.13 0.002 -13.02361 -2.983403
|
race#c.wage |
1 | -8.72e-06 .0000133 -0.65 0.513 -.0000349 .0000174
2 | -.0000135 .000013 -1.04 0.296 -.000039 .0000119
|
race#edu |
1 2 | 2.488045 1.259945 1.97 0.048 .016893 4.959198
1 3 | -.1736131 1.376548 -0.13 0.900 -2.87346 2.526234
1 4 | 2.29836 2.10793 1.09 0.276 -1.835959 6.432679
2 2 | 1.029303 1.46414 0.70 0.482 -1.842342 3.900947
2 3 | -.0586898 1.510437 -0.04 0.969 -3.021136 2.903756
2 4 | 2.118492 2.158164 0.98 0.326 -2.114354 6.351337
|
urban#c.wage |
1 | 8.06e-06 .0000115 0.70 0.482 -.0000144 .0000306
|
urban#edu |
1 2 | .8233918 1.191193 0.69 0.490 -1.512916 3.1597
1 3 | 1.802902 1.29168 1.40 0.163 -.7304935 4.336297
1 4 | -3.443128 2.15643 -1.60 0.111 -7.672571 .7863152
|
female#c.wage |
1 | -.0000233 .0000115 -2.02 0.044 -.000046 -6.71e-07
|
female#edu |
1 2 | .5626791 1.188512 0.47 0.636 -1.768369 2.893728
1 3 | .4449296 1.252109 0.36 0.722 -2.010853 2.900712
1 4 | 2.712876 1.8349 1.48 0.139 -.8859457 6.311698
|
_cons | 9.321907 1.382308 6.74 0.000 6.610763 12.03305
-------------------------------------------------------------------------------
.
.
. // test imputation model for wage
. regress wage i.race exp i.edu i.urban i.female
Source | SS df MS Number of obs = 1779
-------------+------------------------------ F( 8, 1770) = 145.49
Model | 1.0214e+12 8 1.2767e+11 Prob > F = 0.0000
Residual | 1.5532e+12 1770 877509504 R-squared = 0.3967
-------------+------------------------------ Adj R-squared = 0.3940
Total | 2.5746e+12 1778 1.4480e+09 Root MSE = 29623
------------------------------------------------------------------------------
wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
race |
1 | -4353.207 1744.074 -2.50 0.013 -7773.869 -932.5451
2 | -4939.278 1795.106 -2.75 0.006 -8460.029 -1418.526
|
exp | 1771.135 75.35312 23.50 0.000 1623.344 1918.925
|
edu |
2 | 8345.912 2008.366 4.16 0.000 4406.894 12284.93
3 | 26875.02 2120.707 12.67 0.000 22715.66 31034.37
4 | 44200.82 2833.404 15.60 0.000 38643.65 49757.99
|
1.urban | 3737.22 1535.9 2.43 0.015 724.8518 6749.588
1.female | -19496.65 1477.669 -13.19 0.000 -22394.81 -16598.49
_cons | 37847.82 2566.897 14.74 0.000 32813.35 42882.29
------------------------------------------------------------------------------
. // test for misspecification with rvfplot
. // constraint line indicates wage>=0
. rvfplot
. graph export wage.png, replace
(note: file wage.png not found)
(file wage.png written in PNG format)
. // test interactions
. regress wage (i.race i.urban i.female)##(c.exp i.edu)
Source | SS df MS Number of obs = 1779
-------------+------------------------------ F( 24, 1754) = 51.27
Model | 1.0615e+12 24 4.4230e+10 Prob > F = 0.0000
Residual | 1.5130e+12 1754 862625195 R-squared = 0.4123
-------------+------------------------------ Adj R-squared = 0.4043
Total | 2.5746e+12 1778 1.4480e+09 Root MSE = 29370
------------------------------------------------------------------------------
wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
race |
1 | 409.0987 4761.366 0.09 0.932 -8929.451 9747.648
2 | 40.7097 5452.8 0.01 0.994 -10653.96 10735.38
|
1.urban | 2955.393 4512.636 0.65 0.513 -5895.32 11806.1
1.female | -5986.333 4393.73 -1.36 0.173 -14603.83 2631.167
exp | 2086.047 188.9033 11.04 0.000 1715.547 2456.546
|
edu |
2 | 12596.53 4787.695 2.63 0.009 3206.339 21986.72
3 | 33416.6 5179.464 6.45 0.000 23258.02 43575.17
4 | 29270.41 9233.095 3.17 0.002 11161.38 47379.44
|
race#c.exp |
1 | -367.0251 180.2227 -2.04 0.042 -720.499 -13.55117
2 | -53.25982 182.8739 -0.29 0.771 -411.9335 305.4139
|
race#edu |
1 2 | -482.0457 4541.364 -0.11 0.915 -9389.103 8425.011
1 3 | 1861.724 4875.674 0.38 0.703 -7701.021 11424.47
1 4 | 7840.737 7555.631 1.04 0.300 -6978.253 22659.73
2 2 | -7391.542 5296.316 -1.40 0.163 -17779.3 2996.213
2 3 | -4044.694 5390.587 -0.75 0.453 -14617.35 6527.957
2 4 | 3039.309 7774.405 0.39 0.696 -12208.77 18287.38
|
urban#c.exp |
1 | 119.1172 157.4763 0.76 0.450 -189.7437 427.9781
|
urban#edu |
1 2 | 644.4913 4304.863 0.15 0.881 -7798.712 9087.694
1 3 | -7540.896 4566.843 -1.65 0.099 -16497.92 1416.132
1 4 | 25090.89 7625.385 3.29 0.001 10135.09 40046.69
|
female#c.exp |
1 | -547.008 151.0542 -3.62 0.000 -843.2732 -250.7427
|
female#edu |
1 2 | -6295.529 4285.177 -1.47 0.142 -14700.12 2109.064
1 3 | -3410.924 4406.216 -0.77 0.439 -12052.91 5231.064
1 4 | -19915.24 6326.051 -3.15 0.002 -32322.64 -7507.849
|
_cons | 29172.73 5272.194 5.53 0.000 18832.28 39513.17
------------------------------------------------------------------------------
.
.
. // test imputation model for edu
. ologit edu i.race exp wage i.urban i.female
Iteration 0: log likelihood = -2295.6305
Iteration 1: log likelihood = -2021.07
Iteration 2: log likelihood = -2013.1176
Iteration 3: log likelihood = -2013.1071
Iteration 4: log likelihood = -2013.1071
Ordered logistic regression Number of obs = 1779
LR chi2(6) = 565.05
Prob > chi2 = 0.0000
Log likelihood = -2013.1071 Pseudo R2 = 0.1231
------------------------------------------------------------------------------
edu | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
race |
1 | .5023646 .1109327 4.53 0.000 .2849405 .7197886
2 | 1.220383 .1136064 10.74 0.000 .9977182 1.443047
|
exp | -.0604264 .0055268 -10.93 0.000 -.0712587 -.049594
wage | .0000253 1.50e-06 16.89 0.000 .0000224 .0000282
1.urban | .8322351 .0973248 8.55 0.000 .641482 1.022988
1.female | .9733093 .0976488 9.97 0.000 .7819211 1.164697
-------------+----------------------------------------------------------------
/cut1 | .6530809 .1597114 .3400522 .9661095
/cut2 | 2.796932 .1712768 2.461236 3.132628
/cut3 | 5.04024 .2008955 4.646492 5.433988
------------------------------------------------------------------------------
. // test for misspecification by adding interactions
. ologit edu (i.race i.urban i.female)##(c.exp c.wage)
Iteration 0: log likelihood = -2295.6305
Iteration 1: log likelihood = -2013.335
Iteration 2: log likelihood = -2004.1162
Iteration 3: log likelihood = -2004.0949
Iteration 4: log likelihood = -2004.0949
Ordered logistic regression Number of obs = 1779
LR chi2(14) = 583.07
Prob > chi2 = 0.0000
Log likelihood = -2004.0949 Pseudo R2 = 0.1270
-------------------------------------------------------------------------------
edu | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------+----------------------------------------------------------------
race |
1 | .1327298 .2534179 0.52 0.600 -.3639601 .6294198
2 | 1.003797 .2497778 4.02 0.000 .5142413 1.493352
|
1.urban | 1.366727 .2189527 6.24 0.000 .9375876 1.795866
1.female | .618023 .2149086 2.88 0.004 .1968099 1.039236
exp | -.0465445 .0139356 -3.34 0.001 -.0738578 -.0192312
wage | .0000226 3.55e-06 6.38 0.000 .0000157 .0000296
|
race#c.exp |
1 | .0009364 .0132133 0.07 0.944 -.0249611 .0268339
2 | -.0018853 .0134862 -0.14 0.889 -.0283179 .0245472
|
race#c.wage |
1 | 5.02e-06 3.42e-06 1.47 0.142 -1.68e-06 .0000117
2 | 3.62e-06 3.35e-06 1.08 0.280 -2.95e-06 .0000102
|
urban#c.exp |
1 | -.0137359 .0115411 -1.19 0.234 -.0363561 .0088844
|
urban#c.wage |
1 | -4.85e-06 2.88e-06 -1.68 0.092 -.0000105 7.92e-07
|
female#c.exp |
1 | -.0057173 .0108306 -0.53 0.598 -.0269449 .0155103
|
female#c.wage |
1 | 6.55e-06 2.81e-06 2.33 0.020 1.05e-06 .0000121
--------------+----------------------------------------------------------------
/cut1 | .6270383 .2723209 .0932992 1.160777
/cut2 | 2.779004 .2811126 2.228033 3.329974
/cut3 | 5.047488 .2980929 4.463237 5.63174
-------------------------------------------------------------------------------
.
. // test imputation model for urban
. logit urban i.race exp wage i.edu i.female
Iteration 0: log likelihood = -1142.4725
Iteration 1: log likelihood = -1075.0707
Iteration 2: log likelihood = -1073.6056
Iteration 3: log likelihood = -1073.6034
Iteration 4: log likelihood = -1073.6034
Logistic regression Number of obs = 1779
LR chi2(8) = 137.74
Prob > chi2 = 0.0000
Log likelihood = -1073.6034 Pseudo R2 = 0.0603
------------------------------------------------------------------------------
urban | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
race |
1 | -.2954482 .1311706 -2.25 0.024 -.5525378 -.0383585
2 | -.7792647 .1334625 -5.84 0.000 -1.040846 -.517683
|
exp | -.0088777 .0063875 -1.39 0.165 -.021397 .0036416
wage | 4.29e-06 1.77e-06 2.42 0.015 8.23e-07 7.76e-06
|
edu |
2 | .606303 .1394181 4.35 0.000 .3330484 .8795575
3 | 1.03064 .1574484 6.55 0.000 .7220471 1.339233
4 | 1.994752 .2554113 7.81 0.000 1.494155 2.495349
|
1.female | -.0520909 .1138005 -0.46 0.647 -.2751358 .170954
_cons | .1577303 .1863126 0.85 0.397 -.2074357 .5228963
------------------------------------------------------------------------------
. // test for misspecification by adding interactions
. logit urban (i.race i.female)##(c.exp c.wage i.edu)
Iteration 0: log likelihood = -1142.4725
Iteration 1: log likelihood = -1020.9319
Iteration 2: log likelihood = -1015.6326
Iteration 3: log likelihood = -1015.3365
Iteration 4: log likelihood = -1015.3347
Iteration 5: log likelihood = -1015.3347
Logistic regression Number of obs = 1779
LR chi2(23) = 254.28
Prob > chi2 = 0.0000
Log likelihood = -1015.3347 Pseudo R2 = 0.1113
-------------------------------------------------------------------------------
urban | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------+----------------------------------------------------------------
race |
1 | -.7713392 .3795508 -2.03 0.042 -1.515245 -.0274332
2 | -1.156397 .4315566 -2.68 0.007 -2.002233 -.3105619
|
1.female | -1.51098 .3321957 -4.55 0.000 -2.162072 -.8598889
exp | -.0231263 .0138578 -1.67 0.095 -.0502871 .0040344
wage | 5.59e-06 3.70e-06 1.51 0.131 -1.66e-06 .0000129
|
edu |
2 | -.2098759 .2750943 -0.76 0.446 -.7490508 .329299
3 | -.0925368 .3264801 -0.28 0.777 -.7324261 .5473525
4 | .4987983 1.103814 0.45 0.651 -1.664638 2.662234
|
race#c.exp |
1 | .0257714 .0168647 1.53 0.126 -.0072828 .0588256
2 | .0187349 .0166967 1.12 0.262 -.01399 .0514598
|
race#c.wage |
1 | -5.02e-07 4.54e-06 -0.11 0.912 -9.40e-06 8.39e-06
2 | 3.07e-06 4.36e-06 0.70 0.481 -5.47e-06 .0000116
|
race#edu |
1 2 | .1629017 .3408843 0.48 0.633 -.5052193 .8310227
1 3 | .0019093 .4077384 0.00 0.996 -.7972432 .8010619
1 4 | -.5458701 1.203649 -0.45 0.650 -2.904979 1.813238
2 2 | .0964406 .3983642 0.24 0.809 -.684339 .8772201
2 3 | -.3518724 .4347531 -0.81 0.418 -1.203973 .500228
2 4 | -.9799584 1.159742 -0.84 0.398 -3.25301 1.293093
|
female#c.exp |
1 | -.0047 .0134151 -0.35 0.726 -.0309931 .0215931
|
female#c.wage |
1 | -4.51e-06 3.73e-06 -1.21 0.227 -.0000118 2.81e-06
|
female#edu |
1 2 | 1.671398 .30319 5.51 0.000 1.077157 2.26564
1 3 | 2.788041 .3342213 8.34 0.000 2.13298 3.443103
1 4 | 4.461757 .5745605 7.77 0.000 3.335639 5.587875
|
_cons | 1.049029 .3234171 3.24 0.001 .4151428 1.682915
-------------------------------------------------------------------------------
.
.
. // refine models after reviewing results
. mi impute chained (logit) urban (mlogit) race (ologit) edu (pmm) exp wage, dryrun by(female)
Performing setup for each by() group:
-> female = 0
Conditional models:
exp: pmm exp i.urban i.race wage i.edu
urban: logit urban exp i.race wage i.edu
race: mlogit race exp i.urban wage i.edu
wage: pmm wage exp i.urban i.race i.edu
edu: ologit edu exp i.urban i.race wage
-> female = 1
Conditional models:
urban: logit urban wage i.race i.edu exp
wage: pmm wage i.urban i.race i.edu exp
race: mlogit race i.urban wage i.edu exp
edu: ologit edu i.urban wage i.race exp
exp: pmm exp i.urban wage i.race i.edu
.
. // test new models for convergence
. bysort female: reg exp i.urban i.race wage i.edu
----------------------------------------------------------------------------------------------------------------------------------
-> female = 0
Source | SS df MS Number of obs = 892
-------------+------------------------------ F( 7, 884) = 52.98
Model | 24670.002 7 3524.286 Prob > F = 0.0000
Residual | 58807.4441 884 66.524258 R-squared = 0.2955
-------------+------------------------------ Adj R-squared = 0.2900
Total | 83477.4461 891 93.689614 Root MSE = 8.1562
------------------------------------------------------------------------------
exp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.urban | -.5563624 .5784369 -0.96 0.336 -1.691632 .5789075
|
race |
1 | 1.799778 .6750693 2.67 0.008 .4748526 3.124704
2 | .8400697 .7025656 1.20 0.232 -.5388214 2.218961
|
wage | .0001445 7.61e-06 18.99 0.000 .0001295 .0001594
|
edu |
2 | -2.074689 .7327518 -2.83 0.005 -3.512825 -.6365529
3 | -5.124519 .816809 -6.27 0.000 -6.72763 -3.521407
4 | -8.313709 1.337503 -6.22 0.000 -10.93876 -5.688656
|
_cons | 8.402518 .9403253 8.94 0.000 6.556987 10.24805
------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------
-> female = 1
Source | SS df MS Number of obs = 887
-------------+------------------------------ F( 7, 879) = 29.74
Model | 13874.8765 7 1982.12521 Prob > F = 0.0000
Residual | 58577.2689 879 66.6408065 R-squared = 0.1915
-------------+------------------------------ Adj R-squared = 0.1851
Total | 72452.1454 886 81.7744305 Root MSE = 8.1634
------------------------------------------------------------------------------
exp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.urban | -.6693439 .6690433 -1.00 0.317 -1.982453 .6437649
|
race |
1 | .9598328 .6860308 1.40 0.162 -.3866169 2.306282
2 | 1.164061 .7025743 1.66 0.098 -.214858 2.54298
|
wage | .000121 8.71e-06 13.89 0.000 .0001039 .0001381
|
edu |
2 | -1.867343 .8689937 -2.15 0.032 -3.572888 -.1617985
3 | -4.84498 .9371085 -5.17 0.000 -6.684211 -3.005748
4 | -7.366648 1.147116 -6.42 0.000 -9.618054 -5.115242
|
_cons | 8.896763 .8998265 9.89 0.000 7.130704 10.66282
------------------------------------------------------------------------------
. by female: logit urban exp i.race wage i.edu
----------------------------------------------------------------------------------------------------------------------------------
-> female = 0
Iteration 0: log likelihood = -576.14858
Iteration 1: log likelihood = -568.10266
Iteration 2: log likelihood = -568.08745
Iteration 3: log likelihood = -568.08745
Logistic regression Number of obs = 892
LR chi2(7) = 16.12
Prob > chi2 = 0.0240
Log likelihood = -568.08745 Pseudo R2 = 0.0140
------------------------------------------------------------------------------
urban | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exp | -.0083494 .008748 -0.95 0.340 -.0254951 .0087964
|
race |
1 | -.0250462 .1793684 -0.14 0.889 -.3766018 .3265095
2 | -.3721612 .1813618 -2.05 0.040 -.7276238 -.0166986
|
wage | 6.78e-06 2.36e-06 2.88 0.004 2.16e-06 .0000114
|
edu |
2 | -.1155276 .1933929 -0.60 0.550 -.4945708 .2635156
3 | -.2803377 .2178799 -1.29 0.198 -.7073745 .1466991
4 | -.3283938 .3534695 -0.93 0.353 -1.021181 .3643936
|
_cons | .5155881 .2385894 2.16 0.031 .0479615 .9832148
------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------
-> female = 1
Iteration 0: log likelihood = -566.19162
Iteration 1: log likelihood = -450.72498
Iteration 2: log likelihood = -445.73919
Iteration 3: log likelihood = -445.57881
Iteration 4: log likelihood = -445.57813
Iteration 5: log likelihood = -445.57813
Logistic regression Number of obs = 887
LR chi2(7) = 241.23
Prob > chi2 = 0.0000
Log likelihood = -445.57813 Pseudo R2 = 0.2130
------------------------------------------------------------------------------
urban | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exp | -.0107927 .010163 -1.06 0.288 -.0307119 .0091265
|
race |
1 | -.6841661 .2108969 -3.24 0.001 -1.097516 -.2708159
2 | -1.259647 .2157675 -5.84 0.000 -1.682544 -.8367507
|
wage | 1.35e-06 2.93e-06 0.46 0.645 -4.39e-06 7.09e-06
|
edu |
2 | 1.619022 .2371928 6.83 0.000 1.154132 2.083911
3 | 2.681609 .2672965 10.03 0.000 2.157717 3.2055
4 | 4.43645 .4644515 9.55 0.000 3.526142 5.346758
|
_cons | -.531204 .2617202 -2.03 0.042 -1.044166 -.0182419
------------------------------------------------------------------------------
. by female: mlogit race exp i.urban wage i.edu
----------------------------------------------------------------------------------------------------------------------------------
-> female = 0
Iteration 0: log likelihood = -979.43224
Iteration 1: log likelihood = -935.80472
Iteration 2: log likelihood = -934.98446
Iteration 3: log likelihood = -934.97944
Iteration 4: log likelihood = -934.97944
Multinomial logistic regression Number of obs = 892
LR chi2(12) = 88.91
Prob > chi2 = 0.0000
Log likelihood = -934.97944 Pseudo R2 = 0.0454
------------------------------------------------------------------------------
race | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
0 |
exp | -.0274735 .0102909 -2.67 0.008 -.0476434 -.0073037
1.urban | .0262569 .1794679 0.15 0.884 -.3254937 .3780074
wage | 6.28e-06 2.77e-06 2.27 0.023 8.60e-07 .0000117
|
edu |
2 | -.4080685 .206086 -1.98 0.048 -.8119896 -.0041474
3 | -.5017744 .2464439 -2.04 0.042 -.9847955 -.0187533
4 | -1.492324 .5722452 -2.61 0.009 -2.613904 -.3707442
|
_cons | .2462701 .2715063 0.91 0.364 -.2858725 .7784127
-------------+----------------------------------------------------------------
1 | (base outcome)
-------------+----------------------------------------------------------------
2 |
exp | -.0143098 .0102621 -1.39 0.163 -.0344231 .0058036
1.urban | -.3510997 .1744316 -2.01 0.044 -.6929795 -.00922
wage | 9.35e-07 2.75e-06 0.34 0.734 -4.46e-06 6.33e-06
|
edu |
2 | .3857799 .2450301 1.57 0.115 -.0944702 .86603
3 | 1.092693 .2658459 4.11 0.000 .5716446 1.613741
4 | 1.673159 .4094672 4.09 0.000 .8706185 2.4757
|
_cons | -.254461 .2948447 -0.86 0.388 -.832346 .3234241
------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------
-> female = 1
Iteration 0: log likelihood = -973.97087
Iteration 1: log likelihood = -934.18474
Iteration 2: log likelihood = -933.62658
Iteration 3: log likelihood = -933.62647
Iteration 4: log likelihood = -933.62647
Multinomial logistic regression Number of obs = 887
LR chi2(12) = 80.69
Prob > chi2 = 0.0000
Log likelihood = -933.62647 Pseudo R2 = 0.0414
------------------------------------------------------------------------------
race | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
0 |
exp | -.0180992 .0106928 -1.69 0.091 -.0390567 .0028584
1.urban | 1.2596 .2155262 5.84 0.000 .8371769 1.682024
wage | 6.92e-06 3.05e-06 2.27 0.023 9.36e-07 .0000129
|
edu |
2 | -1.015529 .2869699 -3.54 0.000 -1.57798 -.4530781
3 | -1.873085 .3132415 -5.98 0.000 -2.487027 -1.259143
4 | -2.917669 .3964615 -7.36 0.000 -3.694719 -2.140619
|
_cons | .3468113 .282675 1.23 0.220 -.2072216 .9008442
-------------+----------------------------------------------------------------
1 |
exp | -.0030983 .0100496 -0.31 0.758 -.0227951 .0165985
1.urban | .5720785 .1999865 2.86 0.004 .1801122 .9640448
wage | 2.49e-06 2.89e-06 0.86 0.388 -3.17e-06 8.16e-06
|
edu |
2 | -.7094723 .2814518 -2.52 0.012 -1.261108 -.157837
3 | -1.229685 .3028501 -4.06 0.000 -1.823261 -.63611
4 | -1.319989 .3548621 -3.72 0.000 -2.015506 -.6244719
|
_cons | .4289424 .276908 1.55 0.121 -.1137873 .971672
-------------+----------------------------------------------------------------
2 | (base outcome)
------------------------------------------------------------------------------
. by female: reg wage exp i.urban i.race i.edu
----------------------------------------------------------------------------------------------------------------------------------
-> female = 0
Source | SS df MS Number of obs = 892
-------------+------------------------------ F( 7, 884) = 74.03
Model | 4.7872e+11 7 6.8388e+10 Prob > F = 0.0000
Residual | 8.1660e+11 884 923758881 R-squared = 0.3696
-------------+------------------------------ Adj R-squared = 0.3646
Total | 1.2953e+12 891 1.4538e+09 Root MSE = 30393
------------------------------------------------------------------------------
wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exp | 2005.904 105.6246 18.99 0.000 1798.6 2213.208
1.urban | 6217.463 2146.452 2.90 0.004 2004.727 10430.2
|
race |
1 | -5654.544 2518.5 -2.25 0.025 -10597.48 -711.6073
2 | -4775.141 2615.229 -1.83 0.068 -9907.923 357.6405
|
edu |
2 | 10720.55 2719.075 3.94 0.000 5383.955 16057.15
3 | 28231.17 2962.326 9.53 0.000 22417.16 34045.18
4 | 50933.9 4794.995 10.62 0.000 41523 60344.8
|
_cons | 30542.89 3511.689 8.70 0.000 23650.67 37435.11
------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------
-> female = 1
Source | SS df MS Number of obs = 887
-------------+------------------------------ F( 7, 879) = 54.13
Model | 3.1047e+11 7 4.4353e+10 Prob > F = 0.0000
Residual | 7.2028e+11 879 819436657 R-squared = 0.3012
-------------+------------------------------ Adj R-squared = 0.2956
Total | 1.0308e+12 886 1.1634e+09 Root MSE = 28626
------------------------------------------------------------------------------
wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exp | 1488.074 107.0972 13.89 0.000 1277.878 1698.27
1.urban | 1266.729 2347.021 0.54 0.590 -3339.69 5873.148
|
race |
1 | -3442.619 2405.519 -1.43 0.153 -8163.851 1278.613
2 | -5492.975 2460.533 -2.23 0.026 -10322.18 -663.7687
|
edu |
2 | 5988.045 3048.533 1.96 0.050 4.792275 11971.3
3 | 26068.51 3217.693 8.10 0.000 19753.25 32383.77
4 | 41106.03 3875.211 10.61 0.000 33500.29 48711.78
|
_cons | 25087.89 3216.737 7.80 0.000 18774.51 31401.27
------------------------------------------------------------------------------
. by female: ologit edu exp i.urban i.race wage
----------------------------------------------------------------------------------------------------------------------------------
-> female = 0
Iteration 0: log likelihood = -1092.3176
Iteration 1: log likelihood = -986.99706
Iteration 2: log likelihood = -984.5232
Iteration 3: log likelihood = -984.51851
Iteration 4: log likelihood = -984.51851
Ordered logistic regression Number of obs = 892
LR chi2(5) = 215.60
Prob > chi2 = 0.0000
Log likelihood = -984.51851 Pseudo R2 = 0.0987
------------------------------------------------------------------------------
edu | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exp | -.059575 .0078897 -7.55 0.000 -.0750385 -.0441115
1.urban | -.1649053 .1336636 -1.23 0.217 -.4268811 .0970705
|
race |
1 | .420148 .1571087 2.67 0.007 .1122206 .7280753
2 | 1.275139 .1623148 7.86 0.000 .9570078 1.59327
|
wage | .0000242 2.09e-06 11.58 0.000 .0000201 .0000283
-------------+----------------------------------------------------------------
/cut1 | -.1678223 .205886 -.5713513 .2357068
/cut2 | 2.064867 .217243 1.639079 2.490656
/cut3 | 4.553283 .2681615 4.027696 5.07887
------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------
-> female = 1
Iteration 0: log likelihood = -1172.2304
Iteration 1: log likelihood = -973.18412
Iteration 2: log likelihood = -964.74324
Iteration 3: log likelihood = -964.72408
Iteration 4: log likelihood = -964.72408
Ordered logistic regression Number of obs = 887
LR chi2(5) = 415.01
Prob > chi2 = 0.0000
Log likelihood = -964.72408 Pseudo R2 = 0.1770
------------------------------------------------------------------------------
edu | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exp | -.0555539 .0078704 -7.06 0.000 -.0709795 -.0401282
1.urban | 1.938634 .1527146 12.69 0.000 1.639319 2.237949
|
race |
1 | .6760943 .1598405 4.23 0.000 .3628128 .9893759
2 | 1.299797 .1621729 8.01 0.000 .981944 1.61765
|
wage | .0000265 2.22e-06 11.93 0.000 .0000221 .0000309
-------------+----------------------------------------------------------------
/cut1 | .5531119 .1965451 .1678906 .9383332
/cut2 | 2.799431 .2172816 2.373567 3.225295
/cut3 | 5.078792 .2594613 4.570257 5.587326
------------------------------------------------------------------------------
. // for real work you would explore misspecification of refined models as well
.
.
. // test convergence of imputation process
. // since by() and savetrace() don't get along right now, we'll remove by() then throw away these imputations and do them with by
> () but no savetrace().
. preserve
. mi impute chained (logit) urban (mlogit) race (ologit) edu (pmm) exp wage = female, add(5) rseed(88) savetrace(extrace, replace)
> burnin(100)
Conditional models:
urban: logit urban i.race exp wage i.edu female
race: mlogit race i.urban exp wage i.edu female
exp: pmm exp i.urban i.race wage i.edu female
wage: pmm wage i.urban i.race exp i.edu female
edu: ologit edu i.urban i.race exp wage female
Performing chained iterations ...
Multivariate imputation Imputations = 5
Chained equations added = 5
Imputed: m=1 through m=5 updated = 0
Initialization: monotone Iterations = 500
burn-in = 100
urban: logistic regression
race: multinomial logistic regression
edu: ordered logistic regression
exp: predictive mean matching
wage: predictive mean matching
------------------------------------------------------------------
| Observations per m
|----------------------------------------------
Variable | Complete Incomplete Imputed | Total
-------------------+-----------------------------------+----------
urban | 2727 273 273 | 3000
race | 2707 293 293 | 3000
edu | 2681 319 319 | 3000
exp | 2707 293 293 | 3000
wage | 2701 299 299 | 3000
------------------------------------------------------------------
(complete + incomplete = total; imputed is the minimum across m
of the number of filled-in observations.)
.
. use extrace, replace
(Summaries of imputed values from -mi impute chained-)
. reshape wide *mean *sd, i(iter) j(m)
(note: j = 1 2 3 4 5)
Data long -> wide
-----------------------------------------------------------------------------
Number of obs. 505 -> 101
Number of variables 12 -> 51
j variable (5 values) m -> (dropped)
xij variables:
urban_mean -> urban_mean1 urban_mean2 ... urban_mean5
race_mean -> race_mean1 race_mean2 ... race_mean5
exp_mean -> exp_mean1 exp_mean2 ... exp_mean5
wage_mean -> wage_mean1 wage_mean2 ... wage_mean5
edu_mean -> edu_mean1 edu_mean2 ... edu_mean5
urban_sd -> urban_sd1 urban_sd2 ... urban_sd5
race_sd -> race_sd1 race_sd2 ... race_sd5
exp_sd -> exp_sd1 exp_sd2 ... exp_sd5
wage_sd -> wage_sd1 wage_sd2 ... wage_sd5
edu_sd -> edu_sd1 edu_sd2 ... edu_sd5
-----------------------------------------------------------------------------
. tsset iter
time variable: iter, 0 to 100
delta: 1 unit
. tsline exp_mean*, title("Mean of Imputed Values of Experience") note("Each line is for one imputation") legend(off)
. graph export conv1.png, replace
(file conv1.png written in PNG format)
. tsline exp_sd*, title("Standard Deviation of Imputed Values of Experience") note("Each line is for one imputation") legend(off)
. graph export conv2.png, replace
(file conv2.png written in PNG format)
. restore
.
.
. // "real" imputation
. mi impute chained (logit) urban (mlogit) race (ologit) edu (pmm) exp wage = i.female, add(5) rseed(88) by(female)
Performing setup for each by() group:
-> female = 0
Conditional models:
exp: pmm exp i.urban i.race wage i.edu i.female
urban: logit urban exp i.race wage i.edu i.female
race: mlogit race exp i.urban wage i.edu i.female
wage: pmm wage exp i.urban i.race i.edu i.female
edu: ologit edu exp i.urban i.race wage i.female
-> female = 1
Conditional models:
urban: logit urban wage i.race i.edu exp i.female
wage: pmm wage i.urban i.race i.edu exp i.female
race: mlogit race i.urban wage i.edu exp i.female
edu: ologit edu i.urban wage i.race exp i.female
exp: pmm exp i.urban wage i.race i.edu i.female
Performing imputation for each by() group:
-> female = 0
Performing chained iterations ...
-> female = 1
Performing chained iterations ...
Multivariate imputation Imputations = 5
Chained equations added = 5
Imputed: m=1 through m=5 updated = 0
Initialization: monotone Iterations = 50
burn-in = 10
urban: logistic regression
race: multinomial logistic regression
edu: ordered logistic regression
exp: predictive mean matching
wage: predictive mean matching
------------------------------------------------------------------
| Observations per m
by() |----------------------------------------------
Variable | Complete Incomplete Imputed | Total
-------------------+-----------------------------------+----------
female = 0 | |
urban | 1369 143 143 | 1512
race | 1364 148 148 | 1512
edu | 1345 167 167 | 1512
exp | 1372 140 140 | 1512
wage | 1347 165 165 | 1512
| |
female = 1 | |
urban | 1358 130 130 | 1488
race | 1343 145 145 | 1488
edu | 1336 152 152 | 1488
exp | 1335 153 153 | 1488
wage | 1354 134 134 | 1488
| |
-------------------+-----------------------------------+----------
Overall | |
urban | 2727 273 273 | 3000
race | 2707 293 293 | 3000
edu | 2681 319 319 | 3000
exp | 2707 293 293 | 3000
wage | 2701 299 299 | 3000
------------------------------------------------------------------
(complete + incomplete = total; imputed is the minimum across m
of the number of filled-in observations.)
.
. // check if imputed values match observed values
. foreach var of varlist urban race edu {
2. mi xeq 0: tab `var'
3. mi xeq 1/5: tab `var' if miss_`var'
4. }
m=0 data:
-> tab urban
urban | Freq. Percent Cum.
------------+-----------------------------------
0 | 921 33.77 33.77
1 | 1,806 66.23 100.00
------------+-----------------------------------
Total | 2,727 100.00
m=1 data:
-> tab urban if miss_urban
urban | Freq. Percent Cum.
------------+-----------------------------------
0 | 102 37.36 37.36
1 | 171 62.64 100.00
------------+-----------------------------------
Total | 273 100.00
m=2 data:
-> tab urban if miss_urban
urban | Freq. Percent Cum.
------------+-----------------------------------
0 | 97 35.53 35.53
1 | 176 64.47 100.00
------------+-----------------------------------
Total | 273 100.00
m=3 data:
-> tab urban if miss_urban
urban | Freq. Percent Cum.
------------+-----------------------------------
0 | 107 39.19 39.19
1 | 166 60.81 100.00
------------+-----------------------------------
Total | 273 100.00
m=4 data:
-> tab urban if miss_urban
urban | Freq. Percent Cum.
------------+-----------------------------------
0 | 102 37.36 37.36
1 | 171 62.64 100.00
------------+-----------------------------------
Total | 273 100.00
m=5 data:
-> tab urban if miss_urban
urban | Freq. Percent Cum.
------------+-----------------------------------
0 | 97 35.53 35.53
1 | 176 64.47 100.00
------------+-----------------------------------
Total | 273 100.00
m=0 data:
-> tab race
race | Freq. Percent Cum.
------------+-----------------------------------
0 | 864 31.92 31.92
1 | 929 34.32 66.24
2 | 914 33.76 100.00
------------+-----------------------------------
Total | 2,707 100.00
m=1 data:
-> tab race if miss_race
race | Freq. Percent Cum.
------------+-----------------------------------
0 | 97 33.11 33.11
1 | 113 38.57 71.67
2 | 83 28.33 100.00
------------+-----------------------------------
Total | 293 100.00
m=2 data:
-> tab race if miss_race
race | Freq. Percent Cum.
------------+-----------------------------------
0 | 107 36.52 36.52
1 | 88 30.03 66.55
2 | 98 33.45 100.00
------------+-----------------------------------
Total | 293 100.00
m=3 data:
-> tab race if miss_race
race | Freq. Percent Cum.
------------+-----------------------------------
0 | 101 34.47 34.47
1 | 98 33.45 67.92
2 | 94 32.08 100.00
------------+-----------------------------------
Total | 293 100.00
m=4 data:
-> tab race if miss_race
race | Freq. Percent Cum.
------------+-----------------------------------
0 | 119 40.61 40.61
1 | 77 26.28 66.89
2 | 97 33.11 100.00
------------+-----------------------------------
Total | 293 100.00
m=5 data:
-> tab race if miss_race
race | Freq. Percent Cum.
------------+-----------------------------------
0 | 76 25.94 25.94
1 | 116 39.59 65.53
2 | 101 34.47 100.00
------------+-----------------------------------
Total | 293 100.00
m=0 data:
-> tab edu
edu | Freq. Percent Cum.
----------------+-----------------------------------
< High School | 511 19.06 19.06
High School | 996 37.15 56.21
Bachelors | 878 32.75 88.96
Advanced Degree | 296 11.04 100.00
----------------+-----------------------------------
Total | 2,681 100.00
m=1 data:
-> tab edu if miss_edu
edu | Freq. Percent Cum.
----------------+-----------------------------------
< High School | 50 15.67 15.67
High School | 135 42.32 57.99
Bachelors | 98 30.72 88.71
Advanced Degree | 36 11.29 100.00
----------------+-----------------------------------
Total | 319 100.00
m=2 data:
-> tab edu if miss_edu
edu | Freq. Percent Cum.
----------------+-----------------------------------
< High School | 53 16.61 16.61
High School | 129 40.44 57.05
Bachelors | 109 34.17 91.22
Advanced Degree | 28 8.78 100.00
----------------+-----------------------------------
Total | 319 100.00
m=3 data:
-> tab edu if miss_edu
edu | Freq. Percent Cum.
----------------+-----------------------------------
< High School | 60 18.81 18.81
High School | 124 38.87 57.68
Bachelors | 105 32.92 90.60
Advanced Degree | 30 9.40 100.00
----------------+-----------------------------------
Total | 319 100.00
m=4 data:
-> tab edu if miss_edu
edu | Freq. Percent Cum.
----------------+-----------------------------------
< High School | 62 19.44 19.44
High School | 124 38.87 58.31
Bachelors | 93 29.15 87.46
Advanced Degree | 40 12.54 100.00
----------------+-----------------------------------
Total | 319 100.00
m=5 data:
-> tab edu if miss_edu
edu | Freq. Percent Cum.
----------------+-----------------------------------
< High School | 55 17.24 17.24
High School | 138 43.26 60.50
Bachelors | 93 29.15 89.66
Advanced Degree | 33 10.34 100.00
----------------+-----------------------------------
Total | 319 100.00
.
. foreach var of varlist wage exp {
2. mi xeq 0: sum `var'
3. mi xeq 1/5: sum `var' if miss_`var'
4. mi xeq 0: kdensity `var'; graph export chk`var'0.png, replace
5. forval i=1/5 {
6. mi xeq `i': kdensity `var' if miss_`var'; graph export chk`var'`i'.png, replace
7. }
8. }
m=0 data:
-> sum wage
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
wage | 2701 71493.95 38104.3 0 227465.2
m=1 data:
-> sum wage if miss_wage
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
wage | 299 73701.88 38620.86 0 192810.8
m=2 data:
-> sum wage if miss_wage
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
wage | 299 75122.22 38976.49 0 193577.9
m=3 data:
-> sum wage if miss_wage
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
wage | 299 73354.54 40547.16 0 193577.9
m=4 data:
-> sum wage if miss_wage
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
wage | 299 75166.36 40163.56 0 193577.9
m=5 data:
-> sum wage if miss_wage
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
wage | 299 75681.66 41793.81 0 198598.6
m=0 data:
-> kdensity wage
-> graph export chkwage0.png, replace
(file chkwage0.png written in PNG format)
m=1 data:
-> kdensity wage if miss_wage
-> graph export chkwage1.png, replace
(file chkwage1.png written in PNG format)
m=2 data:
-> kdensity wage if miss_wage
-> graph export chkwage2.png, replace
(file chkwage2.png written in PNG format)
m=3 data:
-> kdensity wage if miss_wage
-> graph export chkwage3.png, replace
(file chkwage3.png written in PNG format)
m=4 data:
-> kdensity wage if miss_wage
-> graph export chkwage4.png, replace
(file chkwage4.png written in PNG format)
m=5 data:
-> kdensity wage if miss_wage
-> graph export chkwage5.png, replace
(file chkwage5.png written in PNG format)
m=0 data:
-> sum exp
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
exp | 2707 15.57284 9.656566 0 47.8623
m=1 data:
-> sum exp if miss_exp
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
exp | 293 14.98541 10.0319 0 46.35374
m=2 data:
-> sum exp if miss_exp
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
exp | 293 15.42685 10.09567 0 46.35374
m=3 data:
-> sum exp if miss_exp
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
exp | 293 15.19209 9.870792 0 41.14571
m=4 data:
-> sum exp if miss_exp
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
exp | 293 14.67198 10.40626 0 47.8623
m=5 data:
-> sum exp if miss_exp
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
exp | 293 14.94231 9.530698 0 46.35374
m=0 data:
-> kdensity exp
-> graph export chkexp0.png, replace
(file chkexp0.png written in PNG format)
m=1 data:
-> kdensity exp if miss_exp
-> graph export chkexp1.png, replace
(file chkexp1.png written in PNG format)
m=2 data:
-> kdensity exp if miss_exp
-> graph export chkexp2.png, replace
(file chkexp2.png written in PNG format)
m=3 data:
-> kdensity exp if miss_exp
-> graph export chkexp3.png, replace
(file chkexp3.png written in PNG format)
m=4 data:
-> kdensity exp if miss_exp
-> graph export chkexp4.png, replace
(file chkexp4.png written in PNG format)
m=5 data:
-> kdensity exp if miss_exp
-> graph export chkexp5.png, replace
(file chkexp5.png written in PNG format)
.
. save mi1,replace
file mi1.dta saved
. log close
name:
log: \sscc\pubs\mi\miex.log
log type: text
closed on: 17 Aug 2012, 13:11:21
----------------------------------------------------------------------------------------------------------------------------------
Last Revised: 8/17/2012