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Problem set 2.log
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Problem set 2.log
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----------------------------------------------------------------------------------------------
name: <unnamed>
log:
log type: text
opened on: 10 Apr 2017, 23:18:06
.
. ***************************************************************************
. ******************** Questions 1-2 *******************************************
. ***************************************************************************
.
. /*Please look at the attached pdf*/
. ***************************************************************************
. ******************** Question 3 *******************************************
. ***************************************************************************
.
. /*point a*/
. use datarest
. generate birthqtr_=birthqtr-1
.
.
. generate birthdate=birthyear+(birthqtr_/4)
.
.
. quietly tab higraded, nolabel
. quietly tab higraded
.
. gen edyrs=0
. forvalues i=4(1)8 {
2. replace edyrs=`i'-3 if higraded==10*`i' | higraded==(10*`i')+1
3. }
(2,171 real changes made)
(4,809 real changes made)
(7,440 real changes made)
(8,911 real changes made)
(12,469 real changes made)
.
.
. forvalues i=9(1)22 {
2. replace edyrs=`i'-3 if higraded==10*`i' | higraded ==(10*`i')+1 ///
> | higraded==(10*`i')+2
3. }
(25,878 real changes made)
(29,679 real changes made)
(72,393 real changes made)
(78,160 real changes made)
(97,862 real changes made)
(99,059 real changes made)
(826,948 real changes made)
(174,293 real changes made)
(215,538 real changes made)
(107,199 real changes made)
(245,853 real changes made)
(73,929 real changes made)
(62,423 real changes made)
(42,035 real changes made)
.
. replace edyrs=20 if higraded==230
(52,726 real changes made)
.
. save datarest_, replace
file datarest_.dta saved
.
. collapse (mean) edyrs, by (birthdate birthqtr_ )
. gen birthqtr=birthqtr+1
.
. twoway scatter edyrs birthdate if birthdate<=1939.75, mlabel(birthqtr) ///
> mlabcolor(black) msymbol(square) mlabposition(6) mcolor(black) connect(l) ///
> lcolor(black) msize(small) lwidth(medium)
. graph export figure1.png, replace
(file figure1.png written in PNG format)
.
. twoway scatter edyrs birthdate if birthdate>1939.75 & birthdate<=1949.75, ///
> mlabel(birthqtr) mlabcolor(black) msymbol(square) mlabposition(6) ///
> mcolor(black) connect(l) lcolor(black) msize(small) lwidth(medium)
. graph export figure2.png, replace
(file figure2.png written in PNG format)
.
. twoway scatter edyrs birthdate if birthdate>1949.75 & birthdate<=1959.75, ///
> mlabel(birthqtr) mlabcolor(black) msymbol(square) mlabposition(6) ///
> mcolor(black) connect(l) lcolor(black) msize(small) lwidth(medium)
. graph export figure3.png, replace
(file figure3.png written in PNG format)
.
. /*point b*/
. /*Please look at the attached pdf*/
.
. ***************************************************************************
. ******************** Question 4 *******************************************
. ***************************************************************************
. clear all
. use datarest_
.
. /*We generate the dependent variable*/
. quietly tab disabwrk
. quietly tab disabwrk, nolabel
. quietly tab disabtrn
. quietly tab disabtrn, nolabel
.
. gen disab=0
. replace disab=1 if disabwrk==2 | disabwrk==3 | disabtrn==2
(154,793 real changes made)
.
. /*We generate covariates*/
. generate SMSA=0
. replace SMSA=1 if metro==2
(452,208 real changes made)
.
. gen married=0
. replace married=1 if marst==1
(1,417,418 real changes made)
.
. egen region_=group(region)
.
. /*We restrict the sample*/
. drop if birthyear>=1940
(1,716,806 observations deleted)
. drop if bpl>=90
(38,672 observations deleted)
.
. misstable summarize disabwrk disabtrn edyrs
(variables nonmissing or string)
. save datarest_restricted, replace
file datarest_restricted.dta saved
.
. /*point a*/
. regress disab edyrs, robust
Linear regression Number of obs = 496,790
F(1, 496788) = 12467.87
Prob > F = 0.0000
R-squared = 0.0319
Root MSE = .29842
------------------------------------------------------------------------------
| Robust
disab | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
edyrs | -.0155154 .000139 -111.66 0.000 -.0157878 -.0152431
_cons | .2955661 .0019264 153.43 0.000 .2917903 .2993418
------------------------------------------------------------------------------
. outreg2 using mymodels, replace tex ///
> title("Table 1 - Model specifications") ctitle(OLS) ///
> addtext(Regional dummies, Not included, Year dummies, Not included, State of birth dummies,
> Not included) ///
> addnote("The dependent variable is the health dummy disab")
mymodels.tex
dir : seeout
.
. regress disab edyrs SMSA married birthdate ib(9).region_, baselevels robust
Linear regression Number of obs = 496,790
F(12, 496777) = 1490.92
Prob > F = 0.0000
R-squared = 0.0499
Root MSE = .29563
------------------------------------------------------------------------------
| Robust
disab | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
edyrs | -.0142682 .0001366 -104.46 0.000 -.0145359 -.0140005
SMSA | .0046808 .0012177 3.84 0.000 .002294 .0070675
married | -.0960668 .0013391 -71.74 0.000 -.0986914 -.0934422
birthdate | -.0047122 .0001446 -32.58 0.000 -.0049956 -.0044288
|
region_ |
1 | -.0089874 .002089 -4.30 0.000 -.0130818 -.0048931
2 | -.0190829 .001531 -12.46 0.000 -.0220836 -.0160822
3 | -.0134194 .0015043 -8.92 0.000 -.0163678 -.010471
4 | -.0110049 .0018584 -5.92 0.000 -.0146473 -.0073624
5 | .0022213 .0015973 1.39 0.164 -.0009093 .0053519
6 | .0177977 .0021682 8.21 0.000 .0135482 .0220473
7 | -.000109 .001772 -0.06 0.951 -.0035822 .0033641
8 | .0015901 .0021949 0.72 0.469 -.0027118 .005892
9 | 0 (base)
|
_cons | 9.48062 .2798426 33.88 0.000 8.932137 10.0291
------------------------------------------------------------------------------
. outreg2 using mymodels, tex ctitle(OLS) keep(edyrs SMSA married birthdate) ///
> addtext (Regional dummies, Included, Year dummies, Not included, State of birth dummies, Not
> included)
mymodels.tex
dir : seeout
.
. egen birthyear_=group(birthyear)
.
. regress disab edyrs SMSA married birthdate ib(9).region_ ib(10).birthyear_, baselevels robus
> t
Linear regression Number of obs = 496,790
F(21, 496768) = 852.76
Prob > F = 0.0000
R-squared = 0.0500
Root MSE = .29562
------------------------------------------------------------------------------
| Robust
disab | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
edyrs | -.014266 .0001366 -104.45 0.000 -.0145337 -.0139983
SMSA | .0046683 .0012177 3.83 0.000 .0022815 .007055
married | -.0960469 .0013391 -71.72 0.000 -.0986716 -.0934223
birthdate | -.0072337 .001508 -4.80 0.000 -.0101894 -.004278
|
region_ |
1 | -.0089779 .0020889 -4.30 0.000 -.0130721 -.0048837
2 | -.0190901 .001531 -12.47 0.000 -.0220909 -.0160894
3 | -.013409 .0015043 -8.91 0.000 -.0163574 -.0104606
4 | -.0110214 .0018585 -5.93 0.000 -.0146639 -.0073788
5 | .0022274 .0015972 1.39 0.163 -.0009031 .0053578
6 | .0178028 .0021681 8.21 0.000 .0135535 .0220522
7 | -.0000867 .0017721 -0.05 0.961 -.00356 .0033866
8 | .0015676 .0021949 0.71 0.475 -.0027343 .0058696
9 | 0 (base)
|
birthyear_ |
1 | -.0252381 .0136875 -1.84 0.065 -.0520653 .001589
2 | -.0234323 .0122006 -1.92 0.055 -.0473452 .0004805
3 | -.0210187 .0106982 -1.96 0.049 -.0419869 -.0000505
4 | -.0155322 .0092183 -1.68 0.092 -.0335998 .0025355
5 | -.0171678 .0077277 -2.22 0.026 -.0323138 -.0020217
6 | -.0157758 .0062715 -2.52 0.012 -.0280678 -.0034838
7 | -.0130404 .0048312 -2.70 0.007 -.0225094 -.0035715
8 | -.0119303 .003436 -3.47 0.001 -.0186647 -.0051958
9 | -.0039738 .0022396 -1.77 0.076 -.0083634 .0004158
10 | 0 (base)
|
_cons | 14.37407 2.924648 4.91 0.000 8.641854 20.10629
------------------------------------------------------------------------------
. outreg2 using mymodels, tex ctitle(OLS) keep(edyrs SMSA married birthdate) ///
> addtext (Regional dummies, Included, Year dummies, Included, State of birth dummies, Not inc
> luded)
mymodels.tex
dir : seeout
.
. /*point b*/
. /*Please look at the attached pdf*/
.
. /*point c*/
. egen birthqtr__=group(birthqtr_)
.
. ivregress 2sls disab (edyrs=ib(4).birthqtr__), baselevels vce(robust)
Instrumental variables (2SLS) regression Number of obs = 496,790
Wald chi2(1) = 51.02
Prob > chi2 = 0.0000
R-squared = .
Root MSE = .31906
------------------------------------------------------------------------------
| Robust
disab | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
edyrs | -.0478365 .0066969 -7.14 0.000 -.0609622 -.0347108
_cons | .6977659 .0833462 8.37 0.000 .5344104 .8611214
------------------------------------------------------------------------------
Instrumented: edyrs
Instruments: 1.birthqtr__ 2.birthqtr__ 3.birthqtr__
. quietly estat firststage
. return list
matrices:
r(singleresults) : 1 x 8
. matrix A=r(singleresults)
. matrix list A
A[1,8]
c1 c2 c3 c4 c5 c6 c7 c8
r1 .00037478 .00036875 .00037478 62.164736 3 496786 3.551e-40 .
. scalar F1=A[1,4]
. global F1=F1
.
.
. outreg2 using mymodels, tex ctitle(TSLS) addstat(First-stage F-statistic, $F1) ///
> addtext(Regional dummies, Not included, Year dummies, Not included, State of birth dummies,
> Not included, Instruments, Birth quarters)
mymodels.tex
dir : seeout
.
. ivregress 2sls disab SMSA married ib(9).region_ ib(10).birthyear_ (edyrs=ib(4).birthqtr__),
> baselevels vce(robust)
Instrumental variables (2SLS) regression Number of obs = 496,790
Wald chi2(20) = 8512.15
Prob > chi2 = 0.0000
R-squared = .
Root MSE = .31475
------------------------------------------------------------------------------
| Robust
disab | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
edyrs | -.0458092 .0067941 -6.74 0.000 -.0591254 -.032493
SMSA | .0083846 .001516 5.53 0.000 .0054132 .011356
married | -.0717012 .0054259 -13.21 0.000 -.0823359 -.0610666
|
region_ |
1 | -.0231807 .0037904 -6.12 0.000 -.0306097 -.0157516
2 | -.0406711 .0049295 -8.25 0.000 -.0503328 -.0310094
3 | -.0493207 .007894 -6.25 0.000 -.0647927 -.0338487
4 | -.0418865 .0069214 -6.05 0.000 -.0554522 -.0283208
5 | -.0447505 .0102502 -4.37 0.000 -.0648404 -.0246606
6 | -.0529449 .0154064 -3.44 0.001 -.083141 -.0227488
7 | -.0417397 .0091454 -4.56 0.000 -.0596644 -.0238151
8 | -.0107241 .0035327 -3.04 0.002 -.0176481 -.0038001
9 | 0 (base)
|
birthyear_ |
1 | .0183 .0050348 3.63 0.000 .008432 .0281679
2 | .0168556 .0042763 3.94 0.000 .0084743 .025237
3 | .0135738 .0039643 3.42 0.001 .005804 .0213436
4 | .013982 .0035672 3.92 0.000 .0069905 .0209736
5 | .0068766 .0032222 2.13 0.033 .0005613 .0131919
6 | .0038684 .0027356 1.41 0.157 -.0014934 .0092301
7 | .0013671 .0024224 0.56 0.573 -.0033807 .006115
8 | -.0017693 .0020245 -0.87 0.382 -.0057372 .0021986
9 | .0019271 .0018131 1.06 0.288 -.0016265 .0054808
10 | 0 (base)
|
_cons | .7587411 .0890892 8.52 0.000 .5841294 .9333527
------------------------------------------------------------------------------
Instrumented: edyrs
Instruments: SMSA married 1.region_ 2.region_ 3.region_ 4.region_
5.region_ 6.region_ 7.region_ 8.region_ 1.birthyear_
2.birthyear_ 3.birthyear_ 4.birthyear_ 5.birthyear_
6.birthyear_ 7.birthyear_ 8.birthyear_ 9.birthyear_
1.birthqtr__ 2.birthqtr__ 3.birthqtr__
. quietly estat firststage
. return list
matrices:
r(singleresults) : 1 x 8
. matrix B=r(singleresults)
. matrix list B
B[1,8]
c1 c2 c3 c4 c5 c6 c7 c8
r1 .03830992 .03826733 .00036809 61.045971 3 496767 1.884e-39 .
. scalar F2=B[1,4]
. global F2=F2
. outreg2 using mymodels, tex ctitle(TSLS) keep(edyrs SMSA married) ///
> addstat(First-stage F-statistic, $F2) ///
> addtext(Regional dummies, Included, Year dummies, Included, State of birth dummies, Not incl
> uded, Instruments, Birth quarters)
mymodels.tex
dir : seeout
.
.
. ivregress 2sls disab SMSA married ib(9).region_ i.birthyear_ (edyrs=birthqtr__#birthyear_),
> baselevels vce(robust)
Instrumental variables (2SLS) regression Number of obs = 496,790
Wald chi2(20) = 8675.02
Prob > chi2 = 0.0000
R-squared = .
Root MSE = .31054
------------------------------------------------------------------------------
| Robust
disab | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
edyrs | -.0420271 .005946 -7.07 0.000 -.053681 -.0303733
SMSA | .0079395 .0014525 5.47 0.000 .0050926 .0107865
married | -.0746218 .0047892 -15.58 0.000 -.0840085 -.0652351
|
region_ |
1 | -.021474 .0034677 -6.19 0.000 -.0282706 -.0146775
2 | -.0380803 .0043763 -8.70 0.000 -.0466578 -.0295028
3 | -.0450126 .0069479 -6.48 0.000 -.0586302 -.031395
4 | -.0381859 .0061241 -6.24 0.000 -.050189 -.0261828
5 | -.0391181 .0090031 -4.34 0.000 -.0567638 -.0214724
6 | -.0444591 .0135172 -3.29 0.001 -.0709523 -.0179659
7 | -.036753 .0080488 -4.57 0.000 -.0525283 -.0209777
8 | -.0092509 .0032684 -2.83 0.005 -.0156567 -.002845
9 | 0 (base)
|
birthyear_ |
1 | 0 (base)
2 | -.0019234 .0022634 -0.85 0.395 -.0063597 .0025128
3 | -.0053888 .0023458 -2.30 0.022 -.0099865 -.0007911
4 | -.0052407 .0025555 -2.05 0.040 -.0102495 -.000232
5 | -.0125636 .0027278 -4.61 0.000 -.01791 -.0072171
6 | -.0159085 .0030719 -5.18 0.000 -.0219294 -.0098877
7 | -.0186463 .0033542 -5.56 0.000 -.0252204 -.0120723
8 | -.0221469 .0038082 -5.82 0.000 -.0296108 -.014683
9 | -.0188022 .0042769 -4.40 0.000 -.0271847 -.0104197
10 | -.0208943 .0044906 -4.65 0.000 -.0296956 -.012093
|
_cons | .7300467 .0739316 9.87 0.000 .5851435 .87495
------------------------------------------------------------------------------
Instrumented: edyrs
Instruments: SMSA married 1.region_ 2.region_ 3.region_ 4.region_
5.region_ 6.region_ 7.region_ 8.region_ 2.birthyear_
3.birthyear_ 4.birthyear_ 5.birthyear_ 6.birthyear_
7.birthyear_ 8.birthyear_ 9.birthyear_ 10.birthyear_
2.birthqtr__#1b.birthyear_ 2.birthqtr__#2.birthyear_
2.birthqtr__#3.birthyear_ 2.birthqtr__#4.birthyear_
2.birthqtr__#5.birthyear_ 2.birthqtr__#6.birthyear_
2.birthqtr__#7.birthyear_ 2.birthqtr__#8.birthyear_
2.birthqtr__#9.birthyear_ 2.birthqtr__#10.birthyear_
3.birthqtr__#1b.birthyear_ 3.birthqtr__#2.birthyear_
3.birthqtr__#3.birthyear_ 3.birthqtr__#4.birthyear_
3.birthqtr__#5.birthyear_ 3.birthqtr__#6.birthyear_
3.birthqtr__#7.birthyear_ 3.birthqtr__#8.birthyear_
3.birthqtr__#9.birthyear_ 3.birthqtr__#10.birthyear_
4.birthqtr__#1b.birthyear_ 4.birthqtr__#2.birthyear_
4.birthqtr__#3.birthyear_ 4.birthqtr__#4.birthyear_
4.birthqtr__#5.birthyear_ 4.birthqtr__#6.birthyear_
4.birthqtr__#7.birthyear_ 4.birthqtr__#8.birthyear_
4.birthqtr__#9.birthyear_ 4.birthqtr__#10.birthyear_
. quietly estat firststage
. return list
matrices:
r(singleresults) : 1 x 8
. matrix C=r(singleresults)
. matrix list C
C[1,8]
c1 c2 c3 c4 c5 c6 c7 c8
r1 .03842458 .03832973 .00048727 7.9607721 30 496740 2.197e-34 .
. scalar F3=C[1,4]
. global F3=F3
. outreg2 using mymodels, tex ctitle(TSLS) keep(edyrs SMSA married) ///
> addstat(First-stage F-statistic, $F3) ///
> addtext(Regional dummies, Included, Year dummies, Included, State of birth dummies, Not incl
> uded, Instruments, Birth quarter * Birth year)
mymodels.tex
dir : seeout
.
. egen bpl_=group(bpl)
.
.
. ivregress 2sls disab SMSA married ib(51).bpl_ ib(9).region_ i.birthyear_ (edyrs=birthqtr__#i
> b(51).bpl_), baselevels vce(robust)
Instrumental variables (2SLS) regression Number of obs = 496,790
Wald chi2(70) = 10054.52
Prob > chi2 = 0.0000
R-squared = 0.0275
Root MSE = .2991
------------------------------------------------------------------------------
| Robust
disab | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
edyrs | -.0279809 .0040661 -6.88 0.000 -.0359502 -.0200115
SMSA | .007849 .0015724 4.99 0.000 .0047672 .0109308
married | -.0860619 .0032839 -26.21 0.000 -.0924982 -.0796257
|
bpl_ |
1 | -.0203066 .0115155 -1.76 0.078 -.0428766 .0022634
2 | -.0457874 .0232012 -1.97 0.048 -.0912609 -.0003139
3 | -.0287621 .0119791 -2.40 0.016 -.0522406 -.0052835
4 | -.0012611 .0118696 -0.11 0.915 -.024525 .0220029
5 | -.0135633 .0093951 -1.44 0.149 -.0319773 .0048507
6 | -.0101666 .010258 -0.99 0.322 -.0302719 .0099387
7 | -.0146465 .0100175 -1.46 0.144 -.0342805 .0049874
8 | -.0188576 .0146182 -1.29 0.197 -.0475088 .0097937
9 | .000331 .0115946 0.03 0.977 -.022394 .0230559
10 | -.0019476 .0108128 -0.18 0.857 -.0231404 .0192452
11 | -.0240199 .0128636 -1.87 0.062 -.049232 .0011922
12 | -.0111936 .0177929 -0.63 0.529 -.0460671 .0236798
13 | -.0032468 .0108055 -0.30 0.764 -.0244252 .0179315
14 | -.0015272 .0094787 -0.16 0.872 -.0201051 .0170507
15 | -.00769 .0095146 -0.81 0.419 -.0263382 .0109582
16 | -.0118795 .0095732 -1.24 0.215 -.0306426 .0068836
17 | .0016114 .0097913 0.16 0.869 -.0175792 .0208019
18 | -.006534 .0125517 -0.52 0.603 -.0311348 .0180668
19 | -.0208581 .0120097 -1.74 0.082 -.0443968 .0026805
20 | -.0132918 .0113755 -1.17 0.243 -.0355873 .0090037
21 | -.0268249 .0108987 -2.46 0.014 -.048186 -.0054638
22 | -.0011599 .0096559 -0.12 0.904 -.020085 .0177652
23 | .0067523 .0094284 0.72 0.474 -.0117271 .0252317
24 | -.0105985 .0095912 -1.11 0.269 -.0293968 .0081998
25 | -.0219771 .0123166 -1.78 0.074 -.0461172 .0021629
26 | -.0020519 .0096981 -0.21 0.832 -.0210599 .016956
27 | -.0128174 .0110253 -1.16 0.245 -.0344266 .0087917
28 | .0013288 .0099758 0.13 0.894 -.0182233 .020881
29 | -.0007919 .0168555 -0.05 0.963 -.0338281 .0322444
30 | -.0109042 .0121244 -0.90 0.368 -.0346677 .0128592
31 | -.002869 .0097429 -0.29 0.768 -.0219648 .0162267
32 | -.0392474 .0126634 -3.10 0.002 -.0640671 -.0144277
33 | .0116387 .00978 1.19 0.234 -.0075296 .0308071
34 | -.0337869 .0122641 -2.75 0.006 -.057824 -.0097497
35 | -.0169307 .0106029 -1.60 0.110 -.037712 .0038507
36 | .0029233 .0093686 0.31 0.755 -.0154388 .0212853
37 | .0040407 .0099852 0.40 0.686 -.0155299 .0236113
38 | .0107993 .0106556 1.01 0.311 -.0100853 .0316839
39 | -.0034778 .0093349 -0.37 0.709 -.0217739 .0148183
40 | -.0053053 .0112207 -0.47 0.636 -.0272975 .0166869
41 | -.0329864 .0135343 -2.44 0.015 -.0595132 -.0064596
42 | -.0180917 .0105259 -1.72 0.086 -.038722 .0025385
43 | -.0265472 .0119327 -2.22 0.026 -.0499349 -.0031595
44 | -.0291402 .0107952 -2.70 0.007 -.0502984 -.007982
45 | .0082685 .0107012 0.77 0.440 -.0127054 .0292424
46 | .0098128 .0132569 0.74 0.459 -.0161702 .0357958
47 | -.0338283 .012899 -2.62 0.009 -.0591099 -.0085467
48 | -.0080014 .0099573 -0.80 0.422 -.0275174 .0115145
49 | -.0003306 .0116881 -0.03 0.977 -.0232387 .0225776
50 | -.0118634 .0095039 -1.25 0.212 -.0304907 .0067639
51 | 0 (base)
|
region_ |
1 | -.0182228 .0039249 -4.64 0.000 -.0259155 -.0105302
2 | -.0371264 .0051241 -7.25 0.000 -.0471695 -.0270834
3 | -.0336698 .0054953 -6.13 0.000 -.0444404 -.0228992
4 | -.0256628 .0050067 -5.13 0.000 -.0354757 -.0158499
5 | -.0080014 .0028871 -2.77 0.006 -.0136599 -.0023428
6 | -.0035932 .0047903 -0.75 0.453 -.0129819 .0057955
7 | -.009633 .0030192 -3.19 0.001 -.0155504 -.0037156
8 | -.0032109 .002799 -1.15 0.251 -.0086967 .002275
9 | 0 (base)
|
birthyear_ |
1 | 0 (base)
2 | -.0035793 .0021367 -1.68 0.094 -.0077671 .0006085
3 | -.007482 .002201 -3.40 0.001 -.0117959 -.0031682
4 | -.0082429 .0023246 -3.55 0.000 -.0127991 -.0036868
5 | -.0163009 .0024315 -6.70 0.000 -.0210664 -.0115353
6 | -.020998 .0026059 -8.06 0.000 -.0261055 -.0158906
7 | -.0245822 .0027519 -8.93 0.000 -.0299759 -.0191885
8 | -.0294591 .002991 -9.85 0.000 -.0353215 -.0235968
9 | -.0275119 .0032765 -8.40 0.000 -.0339338 -.0210899
10 | -.0301423 .0034062 -8.85 0.000 -.0368184 -.0234662
|
_cons | .563504 .0525246 10.73 0.000 .4605577 .6664503
------------------------------------------------------------------------------
Instrumented: edyrs
Instruments: SMSA married 1.bpl_ 2.bpl_ 3.bpl_ 4.bpl_ 5.bpl_ 6.bpl_ 7.bpl_
8.bpl_ 9.bpl_ 10.bpl_ 11.bpl_ 12.bpl_ 13.bpl_ 14.bpl_ 15.bpl_
16.bpl_ 17.bpl_ 18.bpl_ 19.bpl_ 20.bpl_ 21.bpl_ 22.bpl_
23.bpl_ 24.bpl_ 25.bpl_ 26.bpl_ 27.bpl_ 28.bpl_ 29.bpl_
30.bpl_ 31.bpl_ 32.bpl_ 33.bpl_ 34.bpl_ 35.bpl_ 36.bpl_
37.bpl_ 38.bpl_ 39.bpl_ 40.bpl_ 41.bpl_ 42.bpl_ 43.bpl_
44.bpl_ 45.bpl_ 46.bpl_ 47.bpl_ 48.bpl_ 49.bpl_ 50.bpl_
1.region_ 2.region_ 3.region_ 4.region_ 5.region_ 6.region_
7.region_ 8.region_ 2.birthyear_ 3.birthyear_ 4.birthyear_
5.birthyear_ 6.birthyear_ 7.birthyear_ 8.birthyear_
9.birthyear_ 10.birthyear_ 2.birthqtr__#1.bpl_
2.birthqtr__#2.bpl_ 2.birthqtr__#3.bpl_ 2.birthqtr__#4.bpl_
2.birthqtr__#5.bpl_ 2.birthqtr__#6.bpl_ 2.birthqtr__#7.bpl_
2.birthqtr__#8.bpl_ 2.birthqtr__#9.bpl_ 2.birthqtr__#10.bpl_
2.birthqtr__#11.bpl_ 2.birthqtr__#12.bpl_
2.birthqtr__#13.bpl_ 2.birthqtr__#14.bpl_
2.birthqtr__#15.bpl_ 2.birthqtr__#16.bpl_
2.birthqtr__#17.bpl_ 2.birthqtr__#18.bpl_
2.birthqtr__#19.bpl_ 2.birthqtr__#20.bpl_
2.birthqtr__#21.bpl_ 2.birthqtr__#22.bpl_
2.birthqtr__#23.bpl_ 2.birthqtr__#24.bpl_
2.birthqtr__#25.bpl_ 2.birthqtr__#26.bpl_
2.birthqtr__#27.bpl_ 2.birthqtr__#28.bpl_
2.birthqtr__#29.bpl_ 2.birthqtr__#30.bpl_
2.birthqtr__#31.bpl_ 2.birthqtr__#32.bpl_
2.birthqtr__#33.bpl_ 2.birthqtr__#34.bpl_
2.birthqtr__#35.bpl_ 2.birthqtr__#36.bpl_
2.birthqtr__#37.bpl_ 2.birthqtr__#38.bpl_
2.birthqtr__#39.bpl_ 2.birthqtr__#40.bpl_
2.birthqtr__#41.bpl_ 2.birthqtr__#42.bpl_
2.birthqtr__#43.bpl_ 2.birthqtr__#44.bpl_
2.birthqtr__#45.bpl_ 2.birthqtr__#46.bpl_
2.birthqtr__#47.bpl_ 2.birthqtr__#48.bpl_
2.birthqtr__#49.bpl_ 2.birthqtr__#50.bpl_
2.birthqtr__#51b.bpl_ 3.birthqtr__#1.bpl_ 3.birthqtr__#2.bpl_
3.birthqtr__#3.bpl_ 3.birthqtr__#4.bpl_ 3.birthqtr__#5.bpl_
3.birthqtr__#6.bpl_ 3.birthqtr__#7.bpl_ 3.birthqtr__#8.bpl_
3.birthqtr__#9.bpl_ 3.birthqtr__#10.bpl_ 3.birthqtr__#11.bpl_
3.birthqtr__#12.bpl_ 3.birthqtr__#13.bpl_
3.birthqtr__#14.bpl_ 3.birthqtr__#15.bpl_
3.birthqtr__#16.bpl_ 3.birthqtr__#17.bpl_
3.birthqtr__#18.bpl_ 3.birthqtr__#19.bpl_
3.birthqtr__#20.bpl_ 3.birthqtr__#21.bpl_
3.birthqtr__#22.bpl_ 3.birthqtr__#23.bpl_
3.birthqtr__#24.bpl_ 3.birthqtr__#25.bpl_
3.birthqtr__#26.bpl_ 3.birthqtr__#27.bpl_
3.birthqtr__#28.bpl_ 3.birthqtr__#29.bpl_
3.birthqtr__#30.bpl_ 3.birthqtr__#31.bpl_
3.birthqtr__#32.bpl_ 3.birthqtr__#33.bpl_
3.birthqtr__#34.bpl_ 3.birthqtr__#35.bpl_
3.birthqtr__#36.bpl_ 3.birthqtr__#37.bpl_
3.birthqtr__#38.bpl_ 3.birthqtr__#39.bpl_
3.birthqtr__#40.bpl_ 3.birthqtr__#41.bpl_
3.birthqtr__#42.bpl_ 3.birthqtr__#43.bpl_
3.birthqtr__#44.bpl_ 3.birthqtr__#45.bpl_
3.birthqtr__#46.bpl_ 3.birthqtr__#47.bpl_
3.birthqtr__#48.bpl_ 3.birthqtr__#49.bpl_
3.birthqtr__#50.bpl_ 3.birthqtr__#51b.bpl_
4.birthqtr__#1.bpl_ 4.birthqtr__#2.bpl_ 4.birthqtr__#3.bpl_
4.birthqtr__#4.bpl_ 4.birthqtr__#5.bpl_ 4.birthqtr__#6.bpl_
4.birthqtr__#7.bpl_ 4.birthqtr__#8.bpl_ 4.birthqtr__#9.bpl_
4.birthqtr__#10.bpl_ 4.birthqtr__#11.bpl_
4.birthqtr__#12.bpl_ 4.birthqtr__#13.bpl_
4.birthqtr__#14.bpl_ 4.birthqtr__#15.bpl_
4.birthqtr__#16.bpl_ 4.birthqtr__#17.bpl_
4.birthqtr__#18.bpl_ 4.birthqtr__#19.bpl_
4.birthqtr__#20.bpl_ 4.birthqtr__#21.bpl_
4.birthqtr__#22.bpl_ 4.birthqtr__#23.bpl_
4.birthqtr__#24.bpl_ 4.birthqtr__#25.bpl_
4.birthqtr__#26.bpl_ 4.birthqtr__#27.bpl_
4.birthqtr__#28.bpl_ 4.birthqtr__#29.bpl_
4.birthqtr__#30.bpl_ 4.birthqtr__#31.bpl_
4.birthqtr__#32.bpl_ 4.birthqtr__#33.bpl_
4.birthqtr__#34.bpl_ 4.birthqtr__#35.bpl_
4.birthqtr__#36.bpl_ 4.birthqtr__#37.bpl_
4.birthqtr__#38.bpl_ 4.birthqtr__#39.bpl_
4.birthqtr__#40.bpl_ 4.birthqtr__#41.bpl_
4.birthqtr__#42.bpl_ 4.birthqtr__#43.bpl_
4.birthqtr__#44.bpl_ 4.birthqtr__#45.bpl_
4.birthqtr__#46.bpl_ 4.birthqtr__#47.bpl_
4.birthqtr__#48.bpl_ 4.birthqtr__#49.bpl_
4.birthqtr__#50.bpl_ 4.birthqtr__#51b.bpl_
. quietly estat firststage
.
. return list
matrices:
r(singleresults) : 1 x 8
. matrix D=r(singleresults)
. matrix list D
D[1,8]
c1 c2 c3 c4 c5 c6 c7 c8
r1 .08118677 .080776 .00107435 3.1663298 153 496567 4.601e-36 .
. scalar F4=D[1,4]
. global F4=F4
. outreg2 using mymodels, tex ctitle(TSLS) keep(edyrs SMSA married) ///
> addstat(First-stage F-statistic, $F4) ///
> addtext(Regional dummies, Included, Year dummies, Included, State of birth dummies, Included
> , Instruments, Birth quarter * State of birth )
mymodels.tex
dir : seeout
.
. /*point d-f*/
. /*Please look at the attached pdf*/
.
. capture log close