panel data. assembling the data insheet using marriage-data.csv, c d u "background-data",...
TRANSCRIPT
Panel Data
Assembling the Datainsheet using marriage-data.csv, c
d
u "background-data", clear
d
u "experience-data", clear
u "wage-data", clear
d
reshape long lwage, i(nr) j(year)
sort nr year
merge 1:1 nr year using "marriage-data"
drop _merge
merge 1:1 nr year using "experience-data"
drop _merge
merge n:1 nr using "background-data"
drop _merge
d
sum
save "data-exercise-11-nls", replace
(2) Is the data balanced?
xtset nr year
panel variable: nr (strongly balanced)
time variable: year, 1980 to 1987
delta: 1 unit
What does being balanced mean?
(3) First Step
. reg lwage married
Source | SS df MS Number of obs = 4360
-------------+------------------------------ F( 1, 4358) = 191.75
Model | 52.1141809 1 52.1141809 Prob > F = 0.0000
Residual | 1184.41546 4358 .271779592 R-squared = 0.0421
-------------+------------------------------ Adj R-squared = 0.0419
Total | 1236.52964 4359 .283672779 Root MSE = .52132
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
married | .2203038 .0159094 13.85 0.000 .1891134 .2514942
_cons | 1.552436 .010541 147.28 0.000 1.53177 1.573101
------------------------------------------------------------------------------
(4) Controls
. reg lwage married exper union educ black hisp
Source | SS df MS Number of obs = 4360-------------+------------------------------ F( 6, 4353) = 163.11 Model | 226.971557 6 37.8285928 Prob > F = 0.0000 Residual | 1009.55809 4353 .231922372 R-squared = 0.1836-------------+------------------------------ Adj R-squared = 0.1824 Total | 1236.52964 4359 .283672779 Root MSE = .48158------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- married | .1127231 .0156735 7.19 0.000 .0819951 .1434511 exper | .0501619 .0028974 17.31 0.000 .0444815 .0558423 union | .1836459 .0171274 10.72 0.000 .1500675 .2172243 educ | .1036792 .0045625 22.72 0.000 .0947343 .1126242 black | -.1424234 .023598 -6.04 0.000 -.1886875 -.0961593 hisp | .0127569 .0208347 0.61 0.540 -.0280897 .0536036 _cons | .0225412 .0630948 0.36 0.721 -.1011567 .1462391------------------------------------------------------------------------------
(5) Panel Data. xtreg lwage married exper union educ black hisp, fenote: educ omitted because of collinearitynote: black omitted because of collinearitynote: hisp omitted because of collinearity
Fixed-effects (within) regression Number of obs = 4360Group variable: nr Number of groups = 545
R-sq: within = 0.1672 Obs per group: min = 8 between = 0.0001 avg = 8.0 overall = 0.0513 max = 8
F(3,3812) = 255.03corr(u_i, Xb) = -0.1575 Prob > F = 0.0000
------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- married | .0610384 .0182929 3.34 0.001 .0251736 .0969032 exper | .0598672 .0025835 23.17 0.000 .054802 .0649325 union | .083791 .019414 4.32 0.000 .045728 .1218539 educ | 0 (omitted) black | 0 (omitted) hisp | 0 (omitted) _cons | 1.211888 .0169244 71.61 0.000 1.178706 1.24507-------------+---------------------------------------------------------------- sigma_u | .40514496 sigma_e | .35352815 rho | .56772216 (fraction of variance due to u_i)------------------------------------------------------------------------------F test that all u_i=0: F(544, 3812) = 10.08 Prob > F = 0.0000
Why have ‘black’, ‘educ’ and ‘hisp’ been dropped from the regression?What variation are we working off when we include fixed effects?
CollinearityID Year ID1_FE ID2_FE ID3_FE Income Married Black National
GDP1995_FE
1 1995 1 0 0 100 0 0 500 1
1 1996 1 0 0 100 0 0 600 0
1 1997 1 0 0 125 1 0 700 0
2 1995 0 1 0 200 1 1 500 1
2 1996 0 1 0 175 0 1 600 0
2 1997 0 1 0 175 0 1 700 0
3 1995 0 0 1 150 1 0 500 1
3 1996 0 0 1 300 1 0 600 0
3 1997 0 0 1 200 1 0 700 0
etc
(6) Clusteringxtreg lwage married exper union, fe cluster(nr)
Fixed-effects (within) regression Number of obs = 4360Group variable: nr Number of groups = 545
R-sq: within = 0.1672 Obs per group: min = 8 between = 0.0001 avg = 8.0 overall = 0.0513 max = 8
F(3,544) = 136.41corr(u_i, Xb) = -0.1575 Prob > F = 0.0000
(Std. Err. adjusted for 545 clusters in nr)------------------------------------------------------------------------------ | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- married | .0610384 .0212076 2.88 0.004 .0193796 .1026972 exper | .0598672 .0033717 17.76 0.000 .0532441 .0664904 union | .083791 .0231101 3.63 0.000 .0383951 .1291868 _cons | 1.211888 .0216293 56.03 0.000 1.169401 1.254375-------------+---------------------------------------------------------------- sigma_u | .40514496 sigma_e | .35352815 rho | .56772216 (fraction of variance due to u_i)------------------------------------------------------------------------------
(7) Are dummies equivalent to FE?
. reg lwage married exper union i.nr, cluster(nr)
Linear regression Number of obs = 4360
F( 2, 544) = .
Prob > F = .
R-squared = 0.6147
Root MSE = .35353
(Std. Err. adjusted for 545 clusters in nr)
------------------------------------------------------------------------------
| Robust
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
married | .0610384 .0226704 2.69 0.007 .0165062 .1055706
exper | .0598672 .0036043 16.61 0.000 .0527872 .0669472
union | .083791 .0247041 3.39 0.001 .0352639 .132318
(7) Time FE? Why not include Experience?. xtreg lwage married union i.year, fe cluster(nr)
Fixed-effects (within) regression Number of obs = 4360Group variable: nr Number of groups = 545
R-sq: within = 0.1689 Obs per group: min = 8 between = 0.0789 avg = 8.0 overall = 0.1026 max = 8
F(9,544) = 42.75corr(u_i, Xb) = 0.0455 Prob > F = 0.0000
(Std. Err. adjusted for 545 clusters in nr)------------------------------------------------------------------------------ | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- married | .0583372 .0228114 2.56 0.011 .013528 .1031464 union | .0833697 .0246533 3.38 0.001 .0349423 .1317971 | year | 1981 | .1135489 .0263198 4.31 0.000 .061848 .1652498 1982 | .1676693 .0259521 6.46 0.000 .1166907 .218648 1983 | .2109386 .0266852 7.90 0.000 .1585199 .2633572 1984 | .2784071 .0295839 9.41 0.000 .2202945 .3365197 1985 | .327462 .0289156 11.32 0.000 .270662 .384262 1986 | .3868075 .0302537 12.79 0.000 .327379 .4462359 1987 | .447037 .0292727 15.27 0.000 .3895357 .5045382 | _cons | 1.361709 .0217851 62.51 0.000 1.318915 1.404502-------------+---------------------------------------------------------------- sigma_u | .38216008 sigma_e | .35343397 rho | .53899212 (fraction of variance due to u_i)------------------------------------------------------------------------------
(8) Driven By Divorce?. xtreg lwage married union i.year if everdivorce == 0, fe cluster(nr)
Fixed-effects (within) regression Number of obs = 3792Group variable: nr Number of groups = 474
R-sq: within = 0.1708 Obs per group: min = 8 between = 0.0834 avg = 8.0 overall = 0.1039 max = 8
F(9,473) = 44.98corr(u_i, Xb) = 0.0456 Prob > F = 0.0000
(Std. Err. adjusted for 474 clusters in nr)------------------------------------------------------------------------------ | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- married | .0631041 .0273805 2.30 0.022 .0093017 .1169066 union | .07263 .0250568 2.90 0.004 .0233935 .1218665 | year | 1981 | .1211224 .0271089 4.47 0.000 .0678536 .1743912 1982 | .1672227 .0269524 6.20 0.000 .1142615 .2201839 1983 | .219521 .0275964 7.95 0.000 .1652942 .2737478 1984 | .2828337 .0312869 9.04 0.000 .2213551 .3443122 1985 | .3269934 .0306809 10.66 0.000 .2667057 .3872812 1986 | .3897902 .0324352 12.02 0.000 .3260552 .4535251 1987 | .4581408 .0317265 14.44 0.000 .3957985 .5204831 | _cons | 1.359177 .0218908 62.09 0.000 1.316162 1.402192-------------+---------------------------------------------------------------- sigma_u | .38405618 sigma_e | .35830293 rho | .5346494 (fraction of variance due to u_i)------------------------------------------------------------------------------
(8) Driven by Divorce 2?
• . xtreg lwage married union i.year if everdivorce == 1, fe cluster(nr)
• Fixed-effects (within) regression Number of obs = 568• Group variable: nr Number of groups = 71
• R-sq: within = 0.1663 Obs per group: min = 8• between = 0.1058 avg = 8.0• overall = 0.1110 max = 8
• F(9,70) = 6.21• corr(u_i, Xb) = 0.0697 Prob > F = 0.0000
• (Std. Err. adjusted for 71 clusters in nr)• ------------------------------------------------------------------------------• | Robust• lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]• -------------+----------------------------------------------------------------• married | .0305923 .0349467 0.88 0.384 -.0391067 .1002913• union | .1277057 .0596013 2.14 0.036 .0088347 .2465766• |• year |• 1981 | .064469 .0568619 1.13 0.261 -.0489385 .1778764• 1982 | .16627 .0565055 2.94 0.004 .0535734 .2789667• 1983 | .1538386 .0637881 2.41 0.019 .0266171 .28106• 1984 | .2469232 .0565838 4.36 0.000 .1340703 .3597761• 1985 | .3212491 .0619483 5.19 0.000 .197697 .4448011• 1986 | .3546588 .0628833 5.64 0.000 .2292421 .4800755• 1987 | .3573659 .0668428 5.35 0.000 .2240521 .4906797• |• _cons | 1.38598 .0568407 24.38 0.000 1.272615 1.499346• -------------+----------------------------------------------------------------• sigma_u | .36780709• sigma_e | .31952552• rho | .56989994 (fraction of variance due to u_i)• ------------------------------------------------------------------------------
• .