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cd "D:\Data Storages\Dataset for Learning"
use nlsy.dta,clear
gen id= _n
reshape long anti self pov, i(id)
bys id:gen times = _n
set matsize 11000
xtset id
xtreg anti,mle
------------------------------------------------------------------------------
anti | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.636833 .0557054 29.38 0.000 1.527652 1.746014
-------------+----------------------------------------------------------------
/sigma_u | 1.208859 .0441422 1.125365 1.298547
/sigma_e | 1.01226 .0209977 .9719306 1.054263
rho | .5878254 .0214793 .5453008 .6293423
------------------------------------------------------------------------------
LR test of sigma_u=0: chibar2(01) = 578.26 Prob >= chibar2 = 0.000
dis e(sigma_u)^2
1.4613398
dis e(sigma_e)^2
1.0246701
mixed anti || id:, mle
eststo m1
------------------------------------------------------------------------------
anti | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.636833 .0557054 29.38 0.000 1.527652 1.746014
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Identity |
var(_cons) | 1.461341 .1067235 1.266447 1.686226
-----------------------------+------------------------------------------------
var(Residual) | 1.02467 .0425105 .9446484 1.11147
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 578.26 Prob >= chibar2 = 0.0000
ICC(intraclass correlation coefficient)= var(_cons)/[var(_cons)+var(Residual)] = 1.461341 / (1.461341+1.02467) = .58782564
组内相关系数为58.78%，表明有58.78%的变异是由研究对象差异（组间斜率差异）造成
estat icc
Intraclass correlation
------------------------------------------------------------------------------
Level | ICC Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
id | .5878256 .0214794 .5451833 .6291892
------------------------------------------------------------------------------
mean anti
Mean estimation Number of obs = 1,743
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
anti | 1.636833 .037777 1.56274 1.710926
--------------------------------------------------------------
reg anti
eststo m2
Source | SS df MS Number of obs = 1,743
-------------+---------------------------------- F(0, 1742) = 0.00
Model | 0 0 . Prob > F = .
Residual | 4333.11532 1,742 2.48743704 R-squared = 0.0000
-------------+---------------------------------- Adj R-squared = 0.0000
Total | 4333.11532 1,742 2.48743704 Root MSE = 1.5772
------------------------------------------------------------------------------
anti | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.636833 .037777 43.33 0.000 1.56274 1.710926
------------------------------------------------------------------------------
mixed anti
eststo m3
Mixed-effects ML regression Number of obs = 1,743
Wald chi2(0) = .
Log likelihood = -3266.8666 Prob > chi2 = .
------------------------------------------------------------------------------
anti | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 1.636833 .0377662 43.34 0.000 1.562813 1.710853
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
var(Residual) | 2.48601 .0842111 2.326319 2.656663
------------------------------------------------------------------------------
lrtest m1 m3, stats
Likelihood-ratio test LR chi2(1) = 578.26
(Assumption: m3 nested in m1) Prob > chi2 = 0.0000
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
m3 | 1,743 . -3266.867 2 6537.733 6548.66
m1 | 1,743 . -2977.735 3 5961.47 5977.861
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.
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