The data files used for the examples in this text can be downloaded in a zip file from the Stata Web site. You can then use a program such as zip to unzip the data files.
Example 10.4 on page 261 using jtrain1.dta.
use jtrain1, clear
xtreg lscrap d88 d89 union grant grant_1, i( fcode)
Random-effects GLS regression Number of obs = 162
Group variable (i): fcode Number of groups = 54
R-sq: within = 0.2006 Obs per group: min = 3
between = 0.0206 avg = 3.0
overall = 0.0361 max = 3
Random effects u_i ~ Gaussian Wald chi2(5) = 26.99
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0001
------------------------------------------------------------------------------
lscrap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.0934519 .1091559 -0.86 0.392 -.3073937 .1204898
d89 | -.2698336 .1316496 -2.05 0.040 -.527862 -.0118052
union | .5478021 .410625 1.33 0.182 -.2570081 1.352612
grant | -.214696 .1477838 -1.45 0.146 -.504347 .0749549
grant_1 | -.3770698 .2053516 -1.84 0.066 -.7795515 .0254119
_cons | .4148333 .2434322 1.70 0.088 -.0622851 .8919518
-------------+----------------------------------------------------------------
sigma_u | 1.3900287
sigma_e | .49774421
rho | .88634984 (fraction of variance due to u_i)
------------------------------------------------------------------------------
test grant grant_1
( 1) grant = 0
( 2) grant_1 = 0
chi2( 2) = 3.66
Prob > chi2 = 0.1601
Example 10.5 on page 272 using jtrain1.dta.
xtreg lscrap d88 d89 union grant grant_1, i( fcode) fe
Fixed-effects (within) regression Number of obs = 162
Group variable (i): fcode Number of groups = 54
R-sq: within = 0.2010 Obs per group: min = 3
between = 0.0079 avg = 3.0
overall = 0.0068 max = 3
F(4,104) = 6.54
corr(u_i, Xb) = -0.0714 Prob > F = 0.0001
------------------------------------------------------------------------------
lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.0802157 .1094751 -0.73 0.465 -.297309 .1368776
d89 | -.2472028 .1332183 -1.86 0.066 -.5113797 .0169741
union | (dropped)
grant | -.2523149 .150629 -1.68 0.097 -.5510178 .0463881
grant_1 | -.4215895 .2102 -2.01 0.047 -.8384239 -.0047551
_cons | .5974341 .0677344 8.82 0.000 .4631142 .7317539
-------------+----------------------------------------------------------------
sigma_u | 1.438982
sigma_e | .49774421
rho | .89313867 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(53, 104) = 23.87 Prob > F = 0.0000
test grant grant_1
( 1) grant = 0
( 2) grant_1 = 0
F( 2, 104) = 2.23
Prob > F = 0.1127
Example 10.5 (continued) on page 276.
Notice that Stata does not calculate the robust standard errors for fixed effect models.
Example 10.6 on page 282 using jtrain1.dta.
use jtrain1, clear
tsset fcode year
panel variable: fcode, 410032 to 419486
time variable: year, 1987 to 1989
reg d.lscrap d89 d.grant d.grant_1
Source | SS df MS Number of obs = 108
-------------+------------------------------ F( 3, 104) = 1.31
Model | 1.31104125 3 .43701375 Prob > F = 0.2739
Residual | 34.5904876 104 .332600842 R-squared = 0.0365
-------------+------------------------------ Adj R-squared = 0.0087
Total | 35.9015288 107 .335528307 Root MSE = .57672
------------------------------------------------------------------------------
D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d89 | -.0962081 .1254469 -0.77 0.445 -.344974 .1525578
grant |
D1 | -.222781 .1307423 -1.70 0.091 -.482048 .0364859
grant_1 |
D1 | -.3512459 .2350848 -1.49 0.138 -.817428 .1149362
_cons | -.0906072 .0909695 -1.00 0.322 -.2710032 .0897888
------------------------------------------------------------------------------
test d.grant d.grant_1
( 1) D.grant = 0
( 2) D.grant_1 = 0
F( 2, 104) = 1.53
Prob > F = 0.2215
Example 10.6 (continued) on page 283.
reg d.lscrap d89 d.grant d.grant_1
Source | SS df MS Number of obs = 108
-------------+------------------------------ F( 3, 104) = 1.31
Model | 1.31104125 3 .43701375 Prob > F = 0.2739
Residual | 34.5904876 104 .332600842 R-squared = 0.0365
-------------+------------------------------ Adj R-squared = 0.0087
Total | 35.9015288 107 .335528307 Root MSE = .57672
------------------------------------------------------------------------------
D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d89 | -.0962081 .1254469 -0.77 0.445 -.344974 .1525578
grant |
D1 | -.222781 .1307423 -1.70 0.091 -.482048 .0364859
grant_1 |
D1 | -.3512459 .2350848 -1.49 0.138 -.817428 .1149362
_cons | -.0906072 .0909695 -1.00 0.322 -.2710032 .0897888
------------------------------------------------------------------------------
predict u, res
(363 missing values generated)
reg u l.u
Source | SS df MS Number of obs = 54
-------------+------------------------------ F( 1, 52) = 3.10
Model | .971328577 1 .971328577 Prob > F = 0.0844
Residual | 16.3125173 52 .313702256 R-squared = 0.0562
-------------+------------------------------ Adj R-squared = 0.0380
Total | 17.2838459 53 .3261103 Root MSE = .56009
------------------------------------------------------------------------------
u | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
u |
L1 | .2369063 .1346333 1.76 0.084 -.0332551 .5070678
_cons | 3.30e-10 .0762188 0.00 1.000 -.1529441 .1529442
------------------------------------------------------------------------------