WOOLDRIDGE CROSS-SECTION & PANEL DATA ECONOMETRICS– EXAMPLES
CHAPTER 10 – Panel Data Models
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name: SN
log: myReplications\iiexample10
log type: smcl
opened on: 11 Jun 2019, 14:11:48
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2016). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 15.1.
. * Chapter 10 - Basic Linear Unobserved Effects Panel Data Models
. * Computer Exercises (Problems)
. ******************** SETUP *********************
. // Example 10.1 NA
. // Example 10.2 NA
. // Example 10.3 NA
. // Example 10.4 (RE Estimation of the Effects of Job Training Grants):
. bcuse jtrain, clear nodesc
. xtset fcode year
panel variable: fcode (strongly balanced)
time variable: year, 1987 to 1989
delta: 1 unit
. xtreg lscrap d88 d89 union grant grant_1, re
Random-effects GLS regression Number of obs = 162
Group variable: fcode Number of groups = 54
R-sq: Obs per group:
within = 0.2006 min = 3
between = 0.0206 avg = 3.0
overall = 0.0361 max = 3
Wald chi2(5) = 26.99
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0001
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lscrap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.0934519 .1091559 -0.86 0.392 -.3073936 .1204898
d89 | -.2698336 .1316496 -2.05 0.040 -.527862 -.0118052
union | .5478021 .410625 1.33 0.182 -.2570081 1.352612
grant | -.2146961 .1477838 -1.45 0.146 -.504347 .0749548
grant_1 | -.3770697 .2053516 -1.84 0.066 -.7795514 .025412
_cons | .4148333 .2434322 1.70 0.088 -.0622851 .8919517
-------------+----------------------------------------------------------------
sigma_u | 1.3900287
sigma_e | .4977442
rho | .88634984 (fraction of variance due to u_i)
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. test grant grant_1
( 1) grant = 0
( 2) grant_1 = 0
chi2( 2) = 3.66
Prob > chi2 = 0.1601
. // Example 10.5 (FE Estimation of the Effects of Job Training Grants):
. xtreg lscrap d88 d89 union grant grant_1, fe
note: union omitted because of collinearity
Fixed-effects (within) regression Number of obs = 162
Group variable: fcode Number of groups = 54
R-sq: Obs per group:
within = 0.2010 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
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lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.0802157 .1094751 -0.73 0.465 -.2973089 .1368776
d89 | -.2472028 .1332183 -1.86 0.066 -.5113797 .016974
union | 0 (omitted)
grant | -.2523149 .150629 -1.68 0.097 -.5510178 .046388
grant_1 | -.4215895 .2102 -2.01 0.047 -.8384239 -.0047551
_cons | .597434 .0677344 8.82 0.000 .4631142 .7317539
-------------+----------------------------------------------------------------
sigma_u | 1.438982
sigma_e | .4977442
rho | .89313867 (fraction of variance due to u_i)
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F test that all u_i=0: F(53, 104) = 24.66 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)
. xtreg lscrap d88 d89 union grant grant_1, fe r
note: union omitted because of collinearity
Fixed-effects (within) regression Number of obs = 162
Group variable: fcode Number of groups = 54
R-sq: Obs per group:
within = 0.2010 min = 3
between = 0.0079 avg = 3.0
overall = 0.0068 max = 3
F(4,53) = 7.07
corr(u_i, Xb) = -0.0714 Prob > F = 0.0001
(Std. Err. adjusted for 54 clusters in fcode)
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| Robust
lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.0802157 .0978408 -0.82 0.416 -.2764594 .1160281
d89 | -.2472028 .1967819 -1.26 0.215 -.6418974 .1474917
union | 0 (omitted)
grant | -.2523149 .1434399 -1.76 0.084 -.5400188 .035389
grant_1 | -.4215895 .2824604 -1.49 0.141 -.9881333 .1449543
_cons | .597434 .0638746 9.35 0.000 .4693177 .7255503
-------------+----------------------------------------------------------------
sigma_u | 1.438982
sigma_e | .4977442
rho | .89313867 (fraction of variance due to u_i)
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. // Example 10.6 (FD Estimation of the Effects of Job Training Grants)
. reg d.lscrap d89 d.grant d.grant_1
Source | SS df MS Number of obs = 108
-------------+---------------------------------- F(3, 104) = 1.31
Model | 1.31104171 3 .437013902 Prob > F = 0.2739
Residual | 34.5904836 104 .332600804 R-squared = 0.0365
-------------+---------------------------------- Adj R-squared = 0.0087
Total | 35.9015253 107 .335528274 Root MSE = .57672
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D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d89 | -.0962082 .1254469 -0.77 0.445 -.3449741 .1525577
|
grant |
D1. | -.2227811 .1307423 -1.70 0.091 -.482048 .0364859
|
grant_1 |
D1. | -.3512459 .2350848 -1.49 0.138 -.817428 .1149361
|
_cons | -.0906072 .0909695 -1.00 0.322 -.2710031 .0897888
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. test d.grant d.grant_1
( 1) D.grant = 0
( 2) D.grant_1 = 0
F( 2, 104) = 1.53
Prob > F = 0.2215
. reg d.lscrap d89 d.grant d.grant_1, robust
Linear regression Number of obs = 108
F(3, 104) = 1.95
Prob > F = 0.1254
R-squared = 0.0365
Root MSE = .57672
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| Robust
D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d89 | -.0962082 .1284682 -0.75 0.456 -.3509654 .1585491
|
grant |
D1. | -.2227811 .1238652 -1.80 0.075 -.4684103 .0228482
|
grant_1 |
D1. | -.3512459 .2477216 -1.42 0.159 -.8424871 .1399952
|
_cons | -.0906072 .0846918 -1.07 0.287 -.2585543 .0773399
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. //Example 10.6 (continued)
. reg d.lscrap d89 d.grant d.grant_1
Source | SS df MS Number of obs = 108
-------------+---------------------------------- F(3, 104) = 1.31
Model | 1.31104171 3 .437013902 Prob > F = 0.2739
Residual | 34.5904836 104 .332600804 R-squared = 0.0365
-------------+---------------------------------- Adj R-squared = 0.0087
Total | 35.9015253 107 .335528274 Root MSE = .57672
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D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d89 | -.0962082 .1254469 -0.77 0.445 -.3449741 .1525577
|
grant |
D1. | -.2227811 .1307423 -1.70 0.091 -.482048 .0364859
|
grant_1 |
D1. | -.3512459 .2350848 -1.49 0.138 -.817428 .1149361
|
_cons | -.0906072 .0909695 -1.00 0.322 -.2710031 .0897888
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. predict u, r
(363 missing values generated)
. g u_1 = u[_n-1]
(363 missing values generated)
. reg u u_1
Source | SS df MS Number of obs = 54
-------------+---------------------------------- F(1, 52) = 3.10
Model | .971329776 1 .971329776 Prob > F = 0.0844
Residual | 16.3125141 52 .313702194 R-squared = 0.0562
-------------+---------------------------------- Adj R-squared = 0.0380
Total | 17.2838438 53 .326110261 Root MSE = .56009
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u | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
u_1 | .2369065 .1346333 1.76 0.084 -.0332549 .5070679
_cons | 3.33e-09 .0762188 0.00 1.000 -.1529441 .1529441
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. //Example 10.7 (Job Training Grants)
. *See Example 10.4 above
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. log close
name: SN
log: myReplications\iiexample10
log type: smcl
closed on: 11 Jun 2019, 14:11:50
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