Chapter 14 Advanced Panel Data Methods- Examples
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name: SN
log: ~Wooldridge\intro-econx\iexample14.smcl
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opened on: 17 Jan 2019, 11:20:47
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. * Solomon Negash - Replicating Examples
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.
. * STATA Program, version 15.1.
. * CHAPTER 14. Advanced Panel Data Methods
. * Computer Exercises (Examples)
. ******************** SETUP *********************
. *Example 14.1. Effect of Job Training on Firm Scrap Rates
. u jtrain, clear
. xtset fcode year
panel variable: fcode (strongly balanced)
time variable: year, 1987 to 1989
delta: 1 unit
. xtreg lscrap d88 d89 grant grant_1, fe
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 -.297309 .1368776
d89 | -.2472028 .1332183 -1.86 0.066 -.5113797 .0169741
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)
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F test that all u_i=0: F(53, 104) = 24.66 Prob > F = 0.0000
. display exp(_b[grant_1])-1
-.34399673
. xtreg lscrap d88 d89 grant, fe
Fixed-effects (within) regression Number of obs = 162
Group variable: fcode Number of groups = 54
R-sq: Obs per group:
within = 0.1701 min = 3
between = 0.0189 avg = 3.0
overall = 0.0130 max = 3
F(3,105) = 7.18
corr(u_i, Xb) = -0.0109 Prob > F = 0.0002
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lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.140066 .106835 -1.31 0.193 -.3519 .0717681
d89 | -.42704 .0999338 -4.27 0.000 -.6251903 -.2288897
grant | -.0822141 .1262632 -0.65 0.516 -.3325706 .1681424
_cons | .5974341 .0687024 8.70 0.000 .4612098 .7336583
-------------+----------------------------------------------------------------
sigma_u | 1.4283441
sigma_e | .50485774
rho | .88894293 (fraction of variance due to u_i)
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F test that all u_i=0: F(53, 105) = 23.90 Prob > F = 0.0000
. *Example 14.2.Has the Return to Education Changed over Time?
. u wagepan, clear
. xtset nr year
panel variable: nr (strongly balanced)
time variable: year, 1980 to 1987
delta: 1 unit
. xtreg lwage c.educ##i.year union mar, fe
note: educ omitted because of collinearity
Fixed-effects (within) regression Number of obs = 4,360
Group variable: nr Number of groups = 545
R-sq: Obs per group:
within = 0.1708 min = 8
between = 0.1900 avg = 8.0
overall = 0.1325 max = 8
F(16,3799) = 48.91
corr(u_i, Xb) = 0.0991 Prob > F = 0.0000
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lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | 0 (omitted)
|
year |
1981 | -.0224158 .1458885 -0.15 0.878 -.3084431 .2636114
1982 | -.0057611 .1458558 -0.04 0.968 -.2917243 .2802021
1983 | .0104297 .1458579 0.07 0.943 -.2755377 .2963971
1984 | .0843743 .1458518 0.58 0.563 -.2015811 .3703297
1985 | .0497253 .1458602 0.34 0.733 -.2362465 .3356971
1986 | .0656064 .1458917 0.45 0.653 -.2204273 .3516401
1987 | .0904448 .1458505 0.62 0.535 -.195508 .3763977
|
year#c.educ |
1981 | .0115854 .0122625 0.94 0.345 -.0124562 .0356271
1982 | .0147905 .0122635 1.21 0.228 -.0092533 .0388342
1983 | .0171182 .0122633 1.40 0.163 -.0069251 .0411615
1984 | .0165839 .0122657 1.35 0.176 -.007464 .0406319
1985 | .0237085 .0122738 1.93 0.053 -.0003554 .0477725
1986 | .0274123 .012274 2.23 0.026 .0033481 .0514765
1987 | .0304332 .0122723 2.48 0.013 .0063722 .0544942
|
union | .0829785 .0194461 4.27 0.000 .0448527 .1211042
married | .0548205 .0184126 2.98 0.003 .018721 .09092
_cons | 1.362459 .0162385 83.90 0.000 1.330622 1.394296
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sigma_u | .37264193
sigma_e | .35335713
rho | .52654439 (fraction of variance due to u_i)
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F test that all u_i=0: F(544, 3799) = 8.09 Prob > F = 0.0000
. testparm c.educ#i.year
( 1) 1981.year#c.educ = 0
( 2) 1982.year#c.educ = 0
( 3) 1983.year#c.educ = 0
( 4) 1984.year#c.educ = 0
( 5) 1985.year#c.educ = 0
( 6) 1986.year#c.educ = 0
( 7) 1987.year#c.educ = 0
F( 7, 3799) = 1.24
Prob > F = 0.2787
. *Example 14.3.Effect of Job Training on Firm Scrap Rates
. u jtrain, clear
. xtset fcode year
panel variable: fcode (strongly balanced)
time variable: year, 1987 to 1989
delta: 1 unit
. xtreg lscrap d88 d89 grant grant_1 lsales lempl, fe
Fixed-effects (within) regression Number of obs = 148
Group variable: fcode Number of groups = 51
R-sq: Obs per group:
within = 0.2131 min = 1
between = 0.0341 avg = 2.9
overall = 0.0004 max = 3
F(6,91) = 4.11
corr(u_i, Xb) = -0.2258 Prob > F = 0.0011
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lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d88 | -.0039609 .1195487 -0.03 0.974 -.2414296 .2335079
d89 | -.132193 .1536863 -0.86 0.392 -.4374719 .173086
grant | -.2967542 .1570861 -1.89 0.062 -.6087863 .015278
grant_1 | -.5355783 .224206 -2.39 0.019 -.980936 -.0902207
lsales | -.0868577 .2596985 -0.33 0.739 -.6027167 .4290014
lemploy | -.0763679 .3502902 -0.22 0.828 -.7721764 .6194405
_cons | 2.115481 3.10843 0.68 0.498 -4.059034 8.289996
-------------+----------------------------------------------------------------
sigma_u | 1.4415155
sigma_e | .49149057
rho | .89585692 (fraction of variance due to u_i)
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F test that all u_i=0: F(50, 91) = 20.75 Prob > F = 0.0000
. *Example 14.4. A Wage Equation Using Panel Data
. u wagepan, clear
. xtset nr year
panel variable: nr (strongly balanced)
time variable: year, 1980 to 1987
delta: 1 unit
. eststo POLS: qui reg lwage educ black hisp exper expersq mar union i.year
. eststo RE: qui xtreg lwage educ black hisp exper expersq mar union i.year, re
. eststo FE: qui xtreg lwage expersq mar union i.year, fe
. estout, cells(b(nostar fmt(3)) se(par fmt(3))) stats(r2 N, fmt(%9.3f %9.0g) labels ///(
R-squared Observations)) varlabels(_cons constant) varwidth(20) ti("Table 14.2 Three ///
Different Estimators of a Wage Equation (lwage)")
Table 14.2 Three Different Estimators of a Wage Equation (lwage)
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POLS RE FE
b/se b/se b/se
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educ 0.091 0.092
(0.005) (0.011)
black -0.139 -0.139
(0.024) (0.048)
hisp 0.016 0.022
(0.021) (0.043)
exper 0.067 0.106
(0.014) (0.015)
expersq -0.002 -0.005 -0.005
(0.001) (0.001) (0.001)
married 0.108 0.064 0.047
(0.016) (0.017) (0.018)
union 0.182 0.106 0.080
(0.017) (0.018) (0.019)
1980.year 0.000 0.000 0.000
(.) (.) (.)
1981.year 0.058 0.040 0.151
(0.030) (0.025) (0.022)
1982.year 0.063 0.031 0.253
(0.033) (0.032) (0.024)
1983.year 0.062 0.020 0.354
(0.037) (0.042) (0.029)
1984.year 0.090 0.043 0.490
(0.040) (0.051) (0.036)
1985.year 0.109 0.058 0.617
(0.043) (0.061) (0.045)
1986.year 0.142 0.092 0.765
(0.046) (0.071) (0.056)
1987.year 0.174 0.135 0.925
(0.049) (0.081) (0.069)
constant 0.092 0.024 1.426
(0.078) (0.151) (0.018)
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R-squared 0.189 0.181
Observations 4360 4360 4360
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. est clear
. log close
name: SN
log: ~Wooldridge\intro-econx\iexample14.smcl
log type: smcl
closed on: 17 Jan 2019, 11:20:48
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