Chapter 17 - Corner Solution Responses
Examples
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name: SN
log: \iiexample17.smcl
log type: smcl
closed on: 11 May 2020, 19:26:46
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2010). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 16.1.
. * Chapter 17 - Corner Solution Responses
. *********************************************
. // Example 17.2 (Annual Hours Equation for Married Women)
. bcuse mroz, clear nodesc
. eststo Linear_OLS: qui reg hours nwifeinc educ exper* age kidslt6 kidsge6
. eststo Tobit_MLE: qui tobit hours nwifeinc educ exper* age kidslt6 kidsge6, ll(0)
. estout Linear_OLS Tobit_MLE, cells(b(nostar fmt(2)) se(par fmt(2))) ///
stats(N, fmt(%9.0g) labels(Observations)) varlabels(_cons constant) ///
varwidth(10) ti("Table 17.1 OLS and Tobit Estimation of Annual Hours Worked: (hours)")
Table 17.1 OLS and Tobit Estimation of Annual Hours Worked: (hours)
------------------------------------
Linear_OLS Tobit_MLE
b/se b/se
------------------------------------
main
nwifeinc -3.45 -8.81
(2.54) (4.46)
educ 28.76 80.65
(12.95) (21.58)
exper 65.67 131.56
(9.96) (17.28)
expersq -0.70 -1.86
(0.32) (0.54)
age -30.51 -54.40
(4.36) (7.42)
kidslt6 -442.09 -894.02
(58.85) (111.88)
kidsge6 -32.78 -16.22
(23.18) (38.64)
constant 1330.48 965.31
(270.78) (446.44)
------------------------------------
/
var..hou~) 1258926.81
(93304.48)
------------------------------------
Observat~s 753 753
------------------------------------
. * APE
. margins, dydx( nwifeinc educ exper* age kidslt6 kidsge6) post pred(ystar(0,.))
Average marginal effects Number of obs = 753
Model VCE : OIM
Expression : E(hours*|hours>0), predict(ystar(0,.))
dy/dx w.r.t. : nwifeinc educ exper expersq age kidslt6 kidsge6
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -5.188618 2.621409 -1.98 0.048 -10.32649 -.0507514
educ | 47.47306 12.6214 3.76 0.000 22.73558 72.21054
exper | 77.44703 9.99765 7.75 0.000 57.85199 97.04206
expersq | -1.09736 .3155945 -3.48 0.001 -1.715914 -.4788063
age | -32.02622 4.29211 -7.46 0.000 -40.4386 -23.61384
kidslt6 | -526.2776 64.70619 -8.13 0.000 -653.0994 -399.4558
kidsge6 | -9.546987 22.75224 -0.42 0.675 -54.14056 35.04659
------------------------------------------------------------------------------
. di "Scale factor = " 5.19/8.81
Scale factor = .58910329
. di "Scale factor = " 47.47/80.65
Scale factor = .58859268
. est clear
. // Example 17.3 (Testing Exogeneity of Other Income in the Hours Equation)
. reg nwifeinc huseduc educ exper expersq age kidslt6 kidsge6
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(7, 745) = 27.13
Model | 20676.7702 7 2953.82432 Prob > F = 0.0000
Residual | 81120.3455 745 108.88637 R-squared = 0.2031
-------------+---------------------------------- Adj R-squared = 0.1956
Total | 101797.116 752 135.368505 Root MSE = 10.435
------------------------------------------------------------------------------
nwifeinc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
huseduc | 1.178155 .1609449 7.32 0.000 .8621956 1.494115
educ | .6746951 .2136829 3.16 0.002 .2552029 1.094187
exper | -.3129878 .1382549 -2.26 0.024 -.5844034 -.0415721
expersq | -.0004776 .0045196 -0.11 0.916 -.0093501 .008395
age | .3401521 .0597084 5.70 0.000 .2229354 .4573687
kidslt6 | .8262718 .8183785 1.01 0.313 -.7803306 2.432874
kidsge6 | .4355289 .3219888 1.35 0.177 -.1965845 1.067642
_cons | -14.72048 3.787326 -3.89 0.000 -22.15559 -7.285382
------------------------------------------------------------------------------
. predict v2, r
. tobit hours nwifeinc educ exper expersq age kidslt6 kidsge6 v2, ll(0) nolog
Tobit regression Number of obs = 753
Uncensored = 428
Limits: lower = 0 Left-censored = 325
upper = +inf Right-censored = 0
LR chi2(8) = 273.76
Prob > chi2 = 0.0000
Log likelihood = -3818.0118 Pseudo R2 = 0.0346
------------------------------------------------------------------------------
hours | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -31.48209 16.03758 -1.96 0.050 -62.96631 .0021284
educ | 116.7811 32.75973 3.56 0.000 52.46875 181.0935
exper | 124.3485 17.87499 6.96 0.000 89.25717 159.4399
expersq | -1.897196 .5371606 -3.53 0.000 -2.951725 -.8426674
age | -46.89236 8.957658 -5.23 0.000 -64.47762 -29.3071
kidslt6 | -867.9116 112.9022 -7.69 0.000 -1089.556 -646.2673
kidsge6 | -6.326127 39.16555 -0.16 0.872 -83.21411 70.56186
v2 | 24.41828 16.5845 1.47 0.141 -8.139631 56.97619
_cons | 722.1052 475.6883 1.52 0.129 -211.7438 1655.954
-------------+----------------------------------------------------------------
var(e.hours)| 1254045 92931.19 1084256 1450421
------------------------------------------------------------------------------
. // Example 17.4 (Annual Hours Equation for Married Women)
. eststo TrancNormual: churdle linear hours nwifeinc educ exper expersq age kidslt6 kidsge6, ///
select(nwifeinc educ exper expersq age kidslt6 kidsge6) ll(0) nolog
Cragg hurdle regression Number of obs = 753
LR chi2(7) = .
Log likelihood = -3791.9498 Prob > chi2 = .
------------------------------------------------------------------------------
hours | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hours |
nwifeinc | .15344 5.164277 0.03 0.976 -9.968357 10.27524
educ | -29.85253 22.83934 -1.31 0.191 -74.61682 14.91175
exper | 72.62271 21.23627 3.42 0.001 31.00039 114.245
expersq | -.9439964 .6090281 -1.55 0.121 -2.13767 .2496767
age | -27.4438 8.293455 -3.31 0.001 -43.69867 -11.18893
kidslt6 | -484.7107 153.788 -3.15 0.002 -786.1297 -183.2918
kidsge6 | -102.6574 43.54345 -2.36 0.018 -188.001 -17.3138
_cons | 2123.516 483.2647 4.39 0.000 1176.334 3070.697
-------------+----------------------------------------------------------------
selection_ll |
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267472 1.266901
-------------+----------------------------------------------------------------
lnsigma |
_cons | 6.746137 .0514841 131.03 0.000 6.64523 6.847044
-------------+----------------------------------------------------------------
/sigma | 850.7657 43.80092 769.1068 941.0948
------------------------------------------------------------------------------
. * Note: The coefficient and SE of age in the participation equation are not the same as in the textbook
. eststo LogNormual: churdle exp hours nwifeinc educ exper expersq age kidslt6 kidsge6,///
select(nwifeinc educ exper expersq age kidslt6 kidsge6) ll(0) nolog
Cragg hurdle regression Number of obs = 753
LR chi2(7) = 304.60
Prob > chi2 = 0.0000
Log likelihood = -3501.6219 Pseudo R2 = 0.0417
------------------------------------------------------------------------------
hours | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hours |
nwifeinc | -.0019676 .0044019 -0.45 0.655 -.0105951 .0066599
educ | -.0385626 .02002 -1.93 0.054 -.0778011 .000676
exper | .073237 .0177323 4.13 0.000 .0384822 .1079917
expersq | -.001233 .0005328 -2.31 0.021 -.0022773 -.0001888
age | -.0236706 .0071799 -3.30 0.001 -.037743 -.0095981
kidslt6 | -.585202 .1174929 -4.98 0.000 -.8154839 -.3549201
kidsge6 | -.0694175 .036985 -1.88 0.061 -.1419067 .0030717
_cons | 7.896267 .4220781 18.71 0.000 7.069009 8.723525
-------------+----------------------------------------------------------------
selection_ll |
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267472 1.266901
-------------+----------------------------------------------------------------
lnsigma |
_cons | -.1232225 .0341793 -3.61 0.000 -.1902127 -.0562323
-------------+----------------------------------------------------------------
/sigma | .884067 .0302168 .8267833 .9453195
------------------------------------------------------------------------------
. g lhours=ln(hours)
(325 missing values generated)
. eststo ET2T: heckman lhours nwifeinc educ exper expersq age kidslt6 kidsge6, ///
select(inlf = nwifeinc educ exper expersq age kidslt6 kidsge6) nolog
Heckman selection model Number of obs = 753
(regression model with sample selection) Selected = 428
Nonselected = 325
Wald chi2(7) = 35.50
Log likelihood = -938.8208 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lhours |
nwifeinc | .0066597 .0050147 1.33 0.184 -.0031689 .0164882
educ | -.1193085 .0242235 -4.93 0.000 -.1667858 -.0718313
exper | -.0334099 .0204429 -1.63 0.102 -.0734773 .0066574
expersq | .0006032 .0006178 0.98 0.329 -.0006077 .0018141
age | .0142754 .0084906 1.68 0.093 -.0023659 .0309167
kidslt6 | .2080079 .1338148 1.55 0.120 -.0542643 .4702801
kidsge6 | -.0920299 .0433138 -2.12 0.034 -.1769235 -.0071364
_cons | 8.670736 .498793 17.38 0.000 7.69312 9.648352
-------------+----------------------------------------------------------------
inlf |
nwifeinc | -.0096823 .0043273 -2.24 0.025 -.0181637 -.001201
educ | .119528 .0217542 5.49 0.000 .0768906 .1621654
exper | .0826696 .0170277 4.86 0.000 .049296 .1160433
expersq | -.0012896 .0005369 -2.40 0.016 -.002342 -.0002372
age | -.0330806 .0075921 -4.36 0.000 -.0479609 -.0182003
kidslt6 | -.5040406 .1074788 -4.69 0.000 -.7146951 -.293386
kidsge6 | .0698201 .0387332 1.80 0.071 -.0060956 .1457357
_cons | -.3656166 .4476569 -0.82 0.414 -1.243008 .5117748
-------------+----------------------------------------------------------------
/athrho | -2.131542 .174212 -12.24 0.000 -2.472991 -1.790093
/lnsigma | .1895611 .0419657 4.52 0.000 .1073099 .2718123
-------------+----------------------------------------------------------------
rho | -.9722333 .0095403 -.9858766 -.9457704
sigma | 1.208719 .0507247 1.113279 1.312341
lambda | -1.175157 .0560391 -1.284991 -1.065322
------------------------------------------------------------------------------
LR test of indep. eqns. (rho = 0): chi2(1) = 34.10 Prob > chi2 = 0.0000
. * esttab TrancNormual LogNormual ET2T, nostar cells(b(nostar fmt(4)) se(par fmt(4))) ///
stats(N, fmt(%9.0g) labels(Observations))
. est clear
. *Alternativelly,
. eststo Participation: probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6, nolog
Probit regression Number of obs = 753
LR chi2(7) = 227.14
Prob > chi2 = 0.0000
Log likelihood = -401.30219 Pseudo R2 = 0.2206
------------------------------------------------------------------------------
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267472 1.266901
------------------------------------------------------------------------------
. eststo NormalHurdle: truncreg hours nwifeinc educ exper expersq age kidslt6 kidsge6, ll(0) nolog
(note: 325 obs. truncated)
Truncated regression
Limit: lower = 0 Number of obs = 428
upper = +inf Wald chi2(7) = 59.05
Log likelihood = -3390.6476 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
hours | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | .15344 5.164279 0.03 0.976 -9.968361 10.27524
educ | -29.85254 22.83935 -1.31 0.191 -74.61684 14.91176
exper | 72.62273 21.23628 3.42 0.001 31.00039 114.2451
expersq | -.9439967 .6090283 -1.55 0.121 -2.13767 .2496769
age | -27.44381 8.293458 -3.31 0.001 -43.69869 -11.18893
kidslt6 | -484.7109 153.7881 -3.15 0.002 -786.13 -183.2918
kidsge6 | -102.6574 43.54347 -2.36 0.018 -188.0011 -17.31379
_cons | 2123.516 483.2649 4.39 0.000 1176.334 3070.697
-------------+----------------------------------------------------------------
/sigma | 850.766 43.80097 19.42 0.000 764.9177 936.6143
------------------------------------------------------------------------------
. eststo Lognormal: truncreg lhours nwifeinc educ exper expersq age kidslt6 kidsge6, ll(0) nolog
(note: 0 obs. truncated)
Truncated regression
Limit: lower = 0 Number of obs = 428
upper = +inf Wald chi2(7) = 84.91
Log likelihood = -554.56647 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
lhours | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0019676 .0044019 -0.45 0.655 -.0105951 .0066599
educ | -.0385626 .02002 -1.93 0.054 -.0778011 .000676
exper | .073237 .0177323 4.13 0.000 .0384822 .1079917
expersq | -.001233 .0005328 -2.31 0.021 -.0022773 -.0001888
age | -.0236706 .0071799 -3.30 0.001 -.037743 -.0095981
kidslt6 | -.585202 .1174929 -4.98 0.000 -.8154839 -.3549201
kidsge6 | -.0694175 .0369849 -1.88 0.061 -.1419067 .0030717
_cons | 7.896267 .422078 18.71 0.000 7.06901 8.723525
-------------+----------------------------------------------------------------
/sigma | .8840669 .0302168 29.26 0.000 .8248431 .9432907
------------------------------------------------------------------------------
. est clear
. *Loglikelihood for selection model
. qui heckman lhours nwifeinc educ exper expersq age kidslt6 kidsge6, ///
select(inlf = nwifeinc educ exper expersq age kidslt6 kidsge6)
. qui sum lhours
. di "Loglikelihood for selection model = " e(ll) - r(mean)*r(N)
Loglikelihood for selection model = -3877.8798
. // Example 17.5 (Panel Data Estimation of Annual Hours Equation for Women)
. u "Wooldridge_2E\psid80_92", clear
. eststo Linear_FE: xtreg hours nwifeinc ch0_2 ch3_5 ch6_17 marr y81-y92, fe cluster(id)
Fixed-effects (within) regression Number of obs = 11,674
Group variable: id Number of groups = 898
R-sq: Obs per group:
within = 0.0719 min = 13
between = 0.0936 avg = 13.0
overall = 0.0855 max = 13
F(17,897) = 15.72
corr(u_i, Xb) = -0.0945 Prob > F = 0.0000
(Std. Err. adjusted for 898 clusters in id)
------------------------------------------------------------------------------
| Robust
hours | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.7752375 .3429502 -2.26 0.024 -1.448316 -.1021593
ch0_2 | -342.3774 26.64763 -12.85 0.000 -394.6763 -290.0784
ch3_5 | -254.1283 25.87788 -9.82 0.000 -304.9165 -203.34
ch6_17 | -42.95787 14.88673 -2.89 0.004 -72.17475 -13.74099
marr | -634.8048 286.1714 -2.22 0.027 -1196.448 -73.1613
y81 | -4.819715 16.29731 -0.30 0.767 -36.80502 27.16559
y82 | -14.88765 21.1851 -0.70 0.482 -56.4658 26.69049
y83 | 6.612531 22.49192 0.29 0.769 -37.53039 50.75545
y84 | 93.79139 25.58646 3.67 0.000 43.5751 144.0077
y85 | 88.73714 25.97019 3.42 0.001 37.76773 139.7065
y86 | 82.66214 27.36886 3.02 0.003 28.94769 136.3766
y87 | 64.28464 27.83649 2.31 0.021 9.652411 118.9169
y88 | 63.79163 29.35211 2.17 0.030 6.184826 121.3984
y89 | 72.98518 30.60838 2.38 0.017 12.91279 133.0576
y90 | 71.24956 31.55331 2.26 0.024 9.322657 133.1765
y91 | 64.67996 32.47097 1.99 0.047 .9520418 128.4079
y92 | 16.01242 33.21255 0.48 0.630 -49.17093 81.19577
_cons | 1786.02 247.297 7.22 0.000 1300.672 2271.368
-------------+----------------------------------------------------------------
sigma_u | 701.66249
sigma_e | 503.92334
rho | .65972225 (fraction of variance due to u_i)
------------------------------------------------------------------------------
. eststo RE_Tobit: xttobit hours nwifeinc ch0_2 ch3_5 ch6_17 marr y81-y92, ll(0) nolog
Random-effects tobit regression Number of obs = 11,674
Uncensored = 8,603
Limits: lower = 0 Left-censored = 3,071
upper = +inf Right-censored = 0
Group variable: id Number of groups = 898
Random effects u_i ~ Gaussian Obs per group:
min = 13
avg = 13.0
max = 13
Integration method: mvaghermite Integration pts. = 12
Wald chi2(17) = 885.58
Log likelihood = -70627.367 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
hours | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -2.020204 .358168 -5.64 0.000 -2.722201 -1.318208
ch0_2 | -460.143 22.4262 -20.52 0.000 -504.0976 -416.1885
ch3_5 | -317.5957 18.94243 -16.77 0.000 -354.7222 -280.4693
ch6_17 | -38.5854 10.41067 -3.71 0.000 -58.98994 -18.18086
marr | -582.5296 83.87442 -6.95 0.000 -746.9205 -418.1388
y81 | -7.102501 31.22654 -0.23 0.820 -68.30539 54.10039
y82 | -39.05952 31.39574 -1.24 0.213 -100.594 22.475
y83 | -9.278637 31.37639 -0.30 0.767 -70.77523 52.21796
y84 | 102.6913 31.3145 3.28 0.001 41.31597 164.0666
y85 | 93.12736 31.4329 2.96 0.003 31.52001 154.7347
y86 | 87.314 31.46283 2.78 0.006 25.648 148.98
y87 | 54.31604 31.65836 1.72 0.086 -7.733207 116.3653
y88 | 60.95545 31.75747 1.92 0.055 -1.288037 123.1989
y89 | 77.71861 31.87821 2.44 0.015 15.23847 140.1988
y90 | 81.53764 31.98205 2.55 0.011 18.85397 144.2213
y91 | 76.12617 32.0872 2.37 0.018 13.23642 139.0159
y92 | 16.92712 32.40571 0.52 0.601 -46.5869 80.44115
_cons | 1607.144 81.81007 19.64 0.000 1446.799 1767.489
-------------+----------------------------------------------------------------
/sigma_u | 989.6984 26.72487 37.03 0.000 937.3186 1042.078
/sigma_e | 613.8584 5.010795 122.51 0.000 604.0374 623.6794
-------------+----------------------------------------------------------------
rho | .7221742 .0111067 .6999993 .743517
------------------------------------------------------------------------------
LR test of sigma_u=0: chibar2(01) = 9502.84 Prob >= chibar2 = 0.000
. eststo CRE_Tobit: xttobit hours nwifeinc ch0_2 ch3_5 ch6_17 marr y81-y92 nwifeincb-marrb, ///
ll(0) nolog
Random-effects tobit regression Number of obs = 11,674
Uncensored = 8,603
Limits: lower = 0 Left-censored = 3,071
upper = +inf Right-censored = 0
Group variable: id Number of groups = 898
Random effects u_i ~ Gaussian Obs per group:
min = 13
avg = 13.0
max = 13
Integration method: mvaghermite Integration pts. = 12
Wald chi2(22) = 933.00
Log likelihood = -70605.118 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
hours | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -1.517148 .3702903 -4.10 0.000 -2.242904 -.7913927
ch0_2 | -472.0622 22.79046 -20.71 0.000 -516.7307 -427.3938
ch3_5 | -329.3407 19.29331 -17.07 0.000 -367.1548 -291.5265
ch6_17 | -46.22098 10.80637 -4.28 0.000 -67.40107 -25.0409
marr | -781.187 153.2584 -5.10 0.000 -1081.568 -480.8061
y81 | -7.528957 31.22892 -0.24 0.809 -68.73651 53.6786
y82 | -39.57472 31.40255 -1.26 0.208 -101.1226 21.97315
y83 | -10.55739 31.39327 -0.34 0.737 -72.08707 50.9723
y84 | 99.67446 31.32943 3.18 0.001 38.26991 161.079
y85 | 88.90949 31.45297 2.83 0.005 27.26279 150.5562
y86 | 81.79348 31.47725 2.60 0.009 20.0992 143.4878
y87 | 48.05343 31.68866 1.52 0.129 -14.0552 110.1621
y88 | 53.01979 31.79586 1.67 0.095 -9.29895 115.3385
y89 | 68.3503 31.92985 2.14 0.032 5.768946 130.9317
y90 | 71.17145 32.03871 2.22 0.026 8.376741 133.9662
y91 | 64.75592 32.15299 2.01 0.044 1.737211 127.7746
y92 | 3.632976 32.49817 0.11 0.911 -60.06226 67.32821
nwifeincb | -6.84578 1.206342 -5.67 0.000 -9.210167 -4.481393
ch0_2b | 122.2966 380.0775 0.32 0.748 -622.6416 867.2349
ch3_5b | 255.7554 367.149 0.70 0.486 -463.8434 975.3543
ch6_17b | 53.03475 56.36095 0.94 0.347 -57.43068 163.5002
marrb | 409.108 188.5929 2.17 0.030 39.47277 778.7432
_cons | 1551.86 94.18517 16.48 0.000 1367.261 1736.46
-------------+----------------------------------------------------------------
/sigma_u | 967.6108 26.05167 37.14 0.000 916.5505 1018.671
/sigma_e | 613.7459 5.008949 122.53 0.000 603.9286 623.5633
-------------+----------------------------------------------------------------
rho | .713102 .01129 .6905808 .7348153
------------------------------------------------------------------------------
LR test of sigma_u=0: chibar2(01) = 9416.39 Prob >= chibar2 = 0.000
. esttab Linear_FE RE_Tobit CRE_Tobit, nostar keep(nwifeinc ch0_2 ch3_5 ch6_17 marr _cons) ///
cells(b(nostar fmt(4)) se(par fmt(4))) stats(ll N r2, fmt(%9.0g) ) ///
ti("Table 17.3 Panel Data Models for Annual Women's Labor Supply, 1980-1992")
Table 17.3 Panel Data Models for Annual Women's Labor Supply, 1980-1992
---------------------------------------------------
Linear_FE RE_Tobit CRE_Tobit
hours hours hours
b/se b/se b/se
---------------------------------------------------
main
nwifeinc -0.7752 -2.0202 -1.5171
(0.3430) (0.3582) (0.3703)
ch0_2 -342.3774 -460.1430 -472.0622
(26.6476) (22.4262) (22.7905)
ch3_5 -254.1283 -317.5957 -329.3407
(25.8779) (18.9424) (19.2933)
ch6_17 -42.9579 -38.5854 -46.2210
(14.8867) (10.4107) (10.8064)
marr -634.8048 -582.5296 -781.1870
(286.1714) (83.8744) (153.2584)
_cons 1786.0197 1607.1439 1551.8602
(247.2970) (81.8101) (94.1852)
---------------------------------------------------
ll -88728.84 -70627.37 -70605.12
N 11674 11674 11674
r2 .0718508
---------------------------------------------------
. log close
name: SN
log: iiexample17.smcl
log type: smcl
closed on: 11 May 2020, 19:27:14
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