Chapter 11 - Topics in Panel Data Models
Examples
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name: SN
log: myReplications\iiexample11.smcl
log type: smcl
opened on: 6 May 2020, 20:52:46
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2010). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 16.1.
. * Chapter 11 - More Topics in Linear Unobserved Effects Models
. ********************************************
. // Example 11.1 (Demand for Air Travel)
. bcuse airfare, clear nodesc
panel variable: id (strongly balanced)
time variable: year, 1997 to 2000
delta: 1 unit
. xtset id year
panel variable: id (strongly balanced)
time variable: year, 1997 to 2000
delta: 1 unit
. eststo RE: qui xtreg lpassen lfare ldist ldistsq i.year, re
. eststo FE: qui xtreg lpassen lfare ldist ldistsq i.year, fe
. eststo REIV: qui xtivreg lpassen (lfare=concen) ldist ldistsq i.year, re
. eststo FEIV: qui xtivreg lpassen (lfare=concen) ldist ldistsq i.year, fe
. estout, keep(lfare ldist ldistsq) cells(b(nostar fmt(4)) se(par fmt(4))) stats(N, ///
fmt(%9.0g) labels(N)) varlabels(_cons constant) varwidth(10) ti("Table 11.1 Passenger ///
Demand Model, United States Domestic Routes, 1997-2000")
Table 11.1 Passenger Demand Model, United States Domestic Routes, 1997-2000
--------------------------------------------------------------
RE FE REIV FEIV
b/se b/se b/se b/se
--------------------------------------------------------------
lfare -1.1025 -1.1550 -0.5079 -0.3016
(0.0220) (0.0228) (0.2297) (0.2774)
ldist -1.9707 0.0000 -1.5048 0.0000
(0.6474) (.) (0.6933) (.)
ldistsq 0.1710 0.0000 0.1176 0.0000
(0.0489) (.) (0.0546) (.)
--------------------------------------------------------------
N 4596 4596 4596 4596
--------------------------------------------------------------
. eststo clear
. // Example 11.2 (Effects of Prison Population on Crime Rates):
. bcuse prison, clear nodesc
. xtset state year
panel variable: state (strongly balanced)
time variable: year, 80 to 93
delta: 1 unit
. global dZ " gpolpc gincpc cag0_14 cag15_17 cag18_24 cag25_34 cunem cblack cmetro "
. *POLS
. reg gpris final1 final2 $dZ i.year, cluster(state)
Linear regression Number of obs = 714
F(24, 50) = 9.27
Prob > F = 0.0000
R-squared = 0.1522
Root MSE = .06237
(Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
| Robust
gpris | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
final1 | -.077488 .0164372 -4.71 0.000 -.1105032 -.0444729
final2 | -.0529558 .0160327 -3.30 0.002 -.0851585 -.0207531
gpolpc | -.0286921 .0305312 -0.94 0.352 -.0900159 .0326316
gincpc | .2095521 .1597362 1.31 0.196 -.1112875 .5303918
cag0_14 | 2.617307 2.029707 1.29 0.203 -1.45948 6.694094
cag15_17 | -1.608738 4.138375 -0.39 0.699 -9.920908 6.703433
cag18_24 | .9533678 1.640538 0.58 0.564 -2.341749 4.248485
cag25_34 | -1.031684 1.945366 -0.53 0.598 -4.939067 2.8757
cunem | .1616595 .280673 0.58 0.567 -.4020888 .7254077
cblack | -.0044763 .0266392 -0.17 0.867 -.0579828 .0490301
cmetro | -1.418389 .7425213 -1.91 0.062 -2.909787 .0730092
|
year |
81 | .0124113 .013231 0.94 0.353 -.0141641 .0389866
82 | .0773503 .0210098 3.68 0.001 .0351508 .1195498
83 | .0767785 .0182621 4.20 0.000 .0400981 .1134589
84 | .0289763 .0187158 1.55 0.128 -.0086156 .0665682
85 | .0279051 .0175829 1.59 0.119 -.0074112 .0632214
86 | .0541489 .0216701 2.50 0.016 .0106233 .0976746
87 | .0312716 .0181202 1.73 0.091 -.0051238 .0676671
88 | .019245 .020214 0.95 0.346 -.0213561 .059846
89 | .0184651 .020502 0.90 0.372 -.0227143 .0596445
90 | .0635926 .0192973 3.30 0.002 .0248328 .1023524
91 | .0263719 .0216737 1.22 0.229 -.017161 .0699049
92 | .0190481 .0207525 0.92 0.363 -.0226345 .0607307
93 | .0134109 .0223509 0.60 0.551 -.0314821 .058304
|
_cons | .0272013 .0224292 1.21 0.231 -.017849 .0722516
------------------------------------------------------------------------------
. test final1 final2
( 1) final1 = 0
( 2) final2 = 0
F( 2, 50) = 18.82
Prob > F = 0.0000
. *2SLS
. ivregress 2sls gcriv (gpris = final1 final2) $dZ i.year, cluster(state)
Instrumental variables (2SLS) regression Number of obs = 714
Wald chi2(23) = 472.56
Prob > chi2 = 0.0000
R-squared = .
Root MSE = .09385
(Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
| Robust
gcriv | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gpris | -1.031956 .207932 -4.96 0.000 -1.439496 -.6244172
gpolpc | .035315 .0535658 0.66 0.510 -.0696721 .1403021
gincpc | .9101992 .3287881 2.77 0.006 .2657864 1.554612
cag0_14 | 3.379384 2.382372 1.42 0.156 -1.28998 8.048748
cag15_17 | 3.549945 5.316435 0.67 0.504 -6.870076 13.96997
cag18_24 | 3.358348 3.162501 1.06 0.288 -2.84004 9.556735
cag25_34 | 2.319993 3.164199 0.73 0.463 -3.881724 8.521709
cunem | .5236958 .4626663 1.13 0.258 -.3831136 1.430505
cblack | -.0158476 .0298869 -0.53 0.596 -.0744248 .0427296
cmetro | -.591517 1.244729 -0.48 0.635 -3.031142 1.848108
|
year |
81 | -.0560732 .0242701 -2.31 0.021 -.1036417 -.0085047
82 | .0284616 .0363238 0.78 0.433 -.0427317 .0996549
83 | .024703 .0329137 0.75 0.453 -.0398066 .0892126
84 | .0128703 .0287645 0.45 0.655 -.0435071 .0692477
85 | .0354026 .0229062 1.55 0.122 -.0094927 .0802979
86 | .0921857 .0310599 2.97 0.003 .0313093 .1530621
87 | .004771 .0285119 0.17 0.867 -.0511112 .0606532
88 | .0532706 .02854 1.87 0.062 -.0026668 .1092079
89 | .0430862 .0310542 1.39 0.165 -.0177789 .1039513
90 | .1442652 .034542 4.18 0.000 .0765642 .2119662
91 | .0618481 .0288607 2.14 0.032 .0052822 .1184139
92 | .0266574 .0304146 0.88 0.381 -.0329542 .086269
93 | .0222739 .0335942 0.66 0.507 -.0435695 .0881174
|
_cons | .0148377 .0350424 0.42 0.672 -.0538441 .0835195
------------------------------------------------------------------------------
Instrumented: gpris
Instruments: gpolpc gincpc cag0_14 cag15_17 cag18_24 cag25_34 cunem cblack
cmetro 81.year 82.year 83.year 84.year 85.year 86.year
87.year 88.year 89.year 90.year 91.year 92.year 93.year
final1 final2
. *POLS on FD
. regress gcriv gpris $dZ i.year, cluster(state)
Linear regression Number of obs = 714
F(23, 50) = 21.54
Prob > F = 0.0000
R-squared = 0.2311
Root MSE = .07893
(Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
| Robust
gcriv | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gpris | -.1808974 .0487909 -3.71 0.001 -.2788967 -.082898
gpolpc | .0514239 .047601 1.08 0.285 -.0441854 .1470333
gincpc | .7383676 .2457843 3.00 0.004 .2446953 1.23204
cag0_14 | .989306 1.86767 0.53 0.599 -2.762019 4.740631
cag15_17 | 4.98384 4.758174 1.05 0.300 -4.573234 14.54091
cag18_24 | 2.412758 3.33858 0.72 0.473 -4.292978 9.118493
cag25_34 | 2.879946 2.61131 1.10 0.275 -2.365025 8.124917
cunem | .41126 .3824013 1.08 0.287 -.3568156 1.179335
cblack | -.0147435 .0147599 -1.00 0.323 -.0443896 .0149027
cmetro | .5383056 1.112491 0.48 0.631 -1.696199 2.77281
|
year |
81 | -.0686258 .0205187 -3.34 0.002 -.1098389 -.0274128
82 | -.0407726 .0245867 -1.66 0.104 -.0901564 .0086112
83 | -.0421775 .0244289 -1.73 0.090 -.0912445 .0068894
84 | -.0136596 .0234198 -0.58 0.562 -.0606996 .0333804
85 | .0094042 .0179408 0.52 0.602 -.026631 .0454394
86 | .0440948 .0223957 1.97 0.055 -.0008883 .0890778
87 | -.0239597 .0233962 -1.02 0.311 -.0709523 .023033
88 | .0347581 .0220287 1.58 0.121 -.0094878 .0790039
89 | .0253571 .0263778 0.96 0.341 -.0276243 .0783385
90 | .0871704 .0253365 3.44 0.001 .0362805 .1380603
91 | .038884 .0198436 1.96 0.056 -.000973 .0787411
92 | .0081502 .022614 0.36 0.720 -.0372713 .0535718
93 | .0087141 .0239587 0.36 0.718 -.0394083 .0568365
|
_cons | -.0056706 .0258729 -0.22 0.827 -.0576377 .0462966
------------------------------------------------------------------------------
. *FD test
. regress gpris final1 final2 $dZ i.year, cluster(state)
Linear regression Number of obs = 714
F(24, 50) = 9.27
Prob > F = 0.0000
R-squared = 0.1522
Root MSE = .06237
(Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
| Robust
gpris | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
final1 | -.077488 .0164372 -4.71 0.000 -.1105032 -.0444729
final2 | -.0529558 .0160327 -3.30 0.002 -.0851585 -.0207531
gpolpc | -.0286921 .0305312 -0.94 0.352 -.0900159 .0326316
gincpc | .2095521 .1597362 1.31 0.196 -.1112875 .5303918
cag0_14 | 2.617307 2.029707 1.29 0.203 -1.45948 6.694094
cag15_17 | -1.608738 4.138375 -0.39 0.699 -9.920908 6.703433
cag18_24 | .9533678 1.640538 0.58 0.564 -2.341749 4.248485
cag25_34 | -1.031684 1.945366 -0.53 0.598 -4.939067 2.8757
cunem | .1616595 .280673 0.58 0.567 -.4020888 .7254077
cblack | -.0044763 .0266392 -0.17 0.867 -.0579828 .0490301
cmetro | -1.418389 .7425213 -1.91 0.062 -2.909787 .0730092
|
year |
81 | .0124113 .013231 0.94 0.353 -.0141641 .0389866
82 | .0773503 .0210098 3.68 0.001 .0351508 .1195498
83 | .0767785 .0182621 4.20 0.000 .0400981 .1134589
84 | .0289763 .0187158 1.55 0.128 -.0086156 .0665682
85 | .0279051 .0175829 1.59 0.119 -.0074112 .0632214
86 | .0541489 .0216701 2.50 0.016 .0106233 .0976746
87 | .0312716 .0181202 1.73 0.091 -.0051238 .0676671
88 | .019245 .020214 0.95 0.346 -.0213561 .059846
89 | .0184651 .020502 0.90 0.372 -.0227143 .0596445
90 | .0635926 .0192973 3.30 0.002 .0248328 .1023524
91 | .0263719 .0216737 1.22 0.229 -.017161 .0699049
92 | .0190481 .0207525 0.92 0.363 -.0226345 .0607307
93 | .0134109 .0223509 0.60 0.551 -.0314821 .058304
|
_cons | .0272013 .0224292 1.21 0.231 -.017849 .0722516
------------------------------------------------------------------------------
. predict u, r
. regress gcriv u gpris $dZ i.year, cluster(state)
Linear regression Number of obs = 714
F(24, 50) = 25.60
Prob > F = 0.0000
R-squared = 0.2400
Root MSE = .07854
(Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
| Robust
gcriv | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
u | .8722033 .2863926 3.05 0.004 .2969669 1.44744
gpris | -1.031956 .2860424 -3.61 0.001 -1.606489 -.4574233
gpolpc | .035315 .0472484 0.75 0.458 -.0595862 .1302162
gincpc | .9101992 .2552867 3.57 0.001 .3974407 1.422958
cag0_14 | 3.379384 1.916788 1.76 0.084 -.4705977 7.229365
cag15_17 | 3.549945 4.68874 0.76 0.453 -5.867667 12.96756
cag18_24 | 3.358348 3.197546 1.05 0.299 -3.064113 9.780809
cag25_34 | 2.319992 2.47294 0.94 0.353 -2.647053 7.287038
cunem | .5236958 .3758529 1.39 0.170 -.2312271 1.278619
cblack | -.0158476 .0150409 -1.05 0.297 -.046058 .0143629
cmetro | -.591517 1.179474 -0.50 0.618 -2.96056 1.777526
|
year |
81 | -.0560732 .0223056 -2.51 0.015 -.1008753 -.0112711
82 | .0284616 .0334096 0.85 0.398 -.0386436 .0955668
83 | .024703 .0329819 0.75 0.457 -.0415431 .0909491
84 | .0128703 .0239878 0.54 0.594 -.0353105 .0610512
85 | .0354026 .0181678 1.95 0.057 -.0010885 .0718937
86 | .0921857 .0243155 3.79 0.000 .0433465 .1410248
87 | .004771 .0235578 0.20 0.840 -.0425463 .0520883
88 | .0532706 .0227985 2.34 0.024 .0074785 .0990627
89 | .0430862 .0256412 1.68 0.099 -.0084157 .0945882
90 | .1442652 .0294413 4.90 0.000 .0851306 .2033998
91 | .0618481 .0204929 3.02 0.004 .0206868 .1030093
92 | .0266574 .0217981 1.22 0.227 -.0171254 .0704402
93 | .0222739 .0235782 0.94 0.349 -.0250844 .0696322
|
_cons | .0148377 .0252115 0.59 0.559 -.0358011 .0654765
------------------------------------------------------------------------------
. // Example 11.3 (Estimating a Dynamic Airfare Equation)
. bcuse airfare, clear nodesc
panel variable: id (strongly balanced)
time variable: year, 1997 to 2000
delta: 1 unit
. xtset id year
panel variable: id (strongly balanced)
time variable: year, 1997 to 2000
delta: 1 unit
. eststo PooledOLS: reg d.lfare l.d.lfare d.concen y99 y00, r
note: y00 omitted because of collinearity
Linear regression Number of obs = 2,298
F(3, 2294) = 42.65
Prob > F = 0.0000
R-squared = 0.0651
Root MSE = .1168
------------------------------------------------------------------------------
| Robust
D.lfare | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lfare |
LD. | -.1264673 .027075 -4.67 0.000 -.1795613 -.0733732
|
concen |
D1. | .0762671 .0488802 1.56 0.119 -.019587 .1721212
|
y99 | -.0473536 .0048179 -9.83 0.000 -.0568015 -.0379056
y00 | 0 (omitted)
_cons | .0624434 .0033052 18.89 0.000 .0559618 .068925
------------------------------------------------------------------------------
. qui reg l.d.lfare l2.lfare if year==1999
. predict ivu1, r
(2,298 missing values generated)
. qui reg l.d.lfare l2.lfare l3.lfare if year==2000
. predict ivu2, r
(3,447 missing values generated)
. eststo PooledIV: ivreg d.lfare (d.l.lfare = ivu1 ivu2 ) d.concen , r
Instrumental variables (2SLS) regression Number of obs = 1,149
F(2, 1146) = 12.10
Prob > F = 0.0000
R-squared = 0.0527
Root MSE = .11039
------------------------------------------------------------------------------
| Robust
D.lfare | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lfare |
LD. | -.2294362 .0493063 -4.65 0.000 -.326177 -.1326955
|
concen |
D1. | -.0929553 .0531162 -1.75 0.080 -.1971712 .0112606
|
_cons | .0629919 .0032858 19.17 0.000 .0565451 .0694387
------------------------------------------------------------------------------
Instrumented: LD.lfare
Instruments: D.concen ivu1 ivu2
------------------------------------------------------------------------------
.
. eststo Arellano_Bond: xtabond lfare concen y00 y99, lag(1)
Arellano-Bond dynamic panel-data estimation Number of obs = 2,298
Group variable: id Number of groups = 1,149
Time variable: year
Obs per group:
min = 2
avg = 2
max = 2
Number of instruments = 7 Wald chi2(4) = 441.62
Prob > chi2 = 0.0000
One-step results
------------------------------------------------------------------------------
lfare | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lfare |
L1. | .3326355 .0548124 6.07 0.000 .2252051 .4400659
|
concen | .1519406 .0399507 3.80 0.000 .0736386 .2302425
y00 | .0629313 .0043475 14.48 0.000 .0544103 .0714523
y99 | .0051715 .0041216 1.25 0.210 -.0029066 .0132496
_cons | 3.304619 .2820506 11.72 0.000 2.75181 3.857428
------------------------------------------------------------------------------
Instruments for differenced equation
GMM-type: L(2/.).lfare
Standard: D.concen D.y00 D.y99
Instruments for level equation
Standard: _cons
. estout PooledOLS PooledIV Arellano_Bond, cells(b(nostar fmt(4)) se(par fmt(4))) stats(N, ///
fmt(%9.0g) labels(N) ti("Table 2.1 Dynamic Airfare Model, First Differencing IV Estimation"))
Table 2.1 Dynamic Airfare Model, First Differencing IV Estimation
---------------------------------------------------
PooledOLS PooledIV Arellano_B~d
b/se b/se b/se
---------------------------------------------------
LD.lfare -0.1265 -0.2294
(0.0271) (0.0493)
L.lfare 0.3326
(0.0548)
D.concen 0.0763 -0.0930
(0.0489) (0.0531)
concen 0.1519
(0.0400)
y99 -0.0474 0.0052
(0.0048) (0.0041)
y00 0.0000 0.0629
(.) (0.0043)
_cons 0.0624 0.0630 3.3046
(0.0033) (0.0033) (0.2821)
---------------------------------------------------
N 2298 1149 2298
---------------------------------------------------
. eststo clear
. // Example 11.4 (Random Growth Model for Analyzing Enterprise Zones)
. bcuse ezunem, clear nodesc
. xtset city year
panel variable: city (strongly balanced)
time variable: year, 1980 to 1988
delta: 1 unit
. xtreg d.luclms d.ez i.year, fe
Fixed-effects (within) regression Number of obs = 176
Group variable: city Number of groups = 22
R-sq: Obs per group:
within = 0.6373 min = 8
between = 0.0094 avg = 8.0
overall = 0.6230 max = 8
F(8,146) = 32.06
corr(u_i, Xb) = -0.0060 Prob > F = 0.0000
------------------------------------------------------------------------------
D.luclms | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ez |
D1. | -.1919386 .0849905 -2.26 0.025 -.3599093 -.023968
|
year |
1982 | .7787587 .0675785 11.52 0.000 .6452003 .9123172
1983 | -.0331199 .0675785 -0.49 0.625 -.1666784 .1004386
1984 | -.0143949 .0714432 -0.20 0.841 -.1555913 .1268016
1985 | .3249093 .0693228 4.69 0.000 .1879036 .461915
1986 | .2921541 .0675785 4.32 0.000 .1585956 .4257126
1987 | .0539477 .0675785 0.80 0.426 -.0796108 .1875062
1988 | -.0170527 .0675785 -0.25 0.801 -.1506111 .1165058
|
_cons | -.3216314 .0477852 -6.73 0.000 -.4160715 -.2271913
-------------+----------------------------------------------------------------
sigma_u | .0524516
sigma_e | .22413255
rho | .05192204 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(21, 146) = 0.44 Prob > F = 0.9849
. *Alternatively
. xtreg guclms cez i.year, fe
Fixed-effects (within) regression Number of obs = 176
Group variable: city Number of groups = 22
R-sq: Obs per group:
within = 0.6373 min = 8
between = 0.0094 avg = 8.0
overall = 0.6230 max = 8
F(8,146) = 32.06
corr(u_i, Xb) = -0.0060 Prob > F = 0.0000
------------------------------------------------------------------------------
guclms | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cez | -.1919402 .0849908 -2.26 0.025 -.3599113 -.023969
|
year |
1982 | .7787595 .0675787 11.52 0.000 .6452006 .9123184
1983 | -.0331192 .0675787 -0.49 0.625 -.1666781 .1004397
1984 | -.0143939 .0714434 -0.20 0.841 -.1555908 .126803
1985 | .3249105 .069323 4.69 0.000 .1879044 .4619167
1986 | .292154 .0675787 4.32 0.000 .1585951 .4257128
1987 | .0539481 .0675787 0.80 0.426 -.0796108 .187507
1988 | -.0170526 .0675787 -0.25 0.801 -.1506115 .1165063
|
_cons | -.3216319 .0477854 -6.73 0.000 -.4160723 -.2271915
-------------+----------------------------------------------------------------
sigma_u | .05245175
sigma_e | .22413321
rho | .05192202 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(21, 146) = 0.44 Prob > F = 0.9849
. // Example 11.5 (Testing for Correlated Random Slopes in a Passenger Demand Equation)
. bcuse airfare, clear nodesc
panel variable: id (strongly balanced)
time variable: year, 1997 to 2000
delta: 1 unit
. xtset id year
panel variable: id (strongly balanced)
time variable: year, 1997 to 2000
delta: 1 unit
. by id: egen mconcen=mean(concen)
. g lfare_cbar = mcon*lfare
. g concen_cbar = mcon*concen
. xtivreg lpassen (lfare lfare_cbar = concen concen_cbar ) i.year, fe vce(cluster id)
Fixed-effects (within) IV regression Number of obs = 4,596
Group variable: id Number of groups = 1,149
R-sq: Obs per group:
within = . min = 4
between = 0.0014 avg = 4.0
overall = 0.0013 max = 4
Wald chi2(5) = 32.82
corr(u_i, Xb) = -0.9965 Prob > chi2 = 0.0000
(Std. Err. adjusted for 1,149 clusters in id)
------------------------------------------------------------------------------
| Robust
lpassen | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lfare | 7.928977 14.48312 0.55 0.584 -20.45742 36.31538
lfare_cbar | -11.04633 18.55033 -0.60 0.552 -47.40431 25.31165
|
year |
1998 | .0138466 .0410113 0.34 0.736 -.0665341 .0942272
1999 | .0725267 .0365297 1.99 0.047 .0009298 .1441236
2000 | .0468379 .1846637 0.25 0.800 -.3150963 .408772
|
_cons | -.2900056 16.64715 -0.02 0.986 -32.91783 32.33782
-------------+----------------------------------------------------------------
sigma_u | 10.772971
sigma_e | .33205881
rho | .99905082 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Instrumented: lfare lfare_cbar
Instruments: 1998.year 1999.year 2000.year concen concen_cbar
------------------------------------------------------------------------------
. log close
name: SN
log: myReplications\iiexample11.smcl
log type: smcl
closed on: 6 May 2020, 20:52:56
-----------------------------------------------------------------------------------------------