Chapter 7 - Examples
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name: SN
log: ~Wooldridge\intro-econx\iexample7.smcl
log type: smcl
opened on: 9 Jan 2019, 00:27:17
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* Solomon Negash - Replicating Examples
* Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.
* STATA Program, version 15.1.
* Chapter 7 - Multiple Regression Analysis with Qualitative Information
* Computer Exercises (Examples)
******************** SETUP *********************
*Example7.1. Hourly wage equation
u wage1, clear
reg wage female educ exper tenure
Source | SS df MS Number of obs = 526
-------------+---------------------------------- F(4, 521) = 74.40
Model | 2603.10658 4 650.776644 Prob > F = 0.0000
Residual | 4557.30771 521 8.7472317 R-squared = 0.3635
-------------+---------------------------------- Adj R-squared = 0.3587
Total | 7160.41429 525 13.6388844 Root MSE = 2.9576
------------------------------------------------------------------------------
wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | -1.810852 .2648252 -6.84 0.000 -2.331109 -1.290596
educ | .5715048 .0493373 11.58 0.000 .4745802 .6684293
exper | .0253959 .0115694 2.20 0.029 .0026674 .0481243
tenure | .1410051 .0211617 6.66 0.000 .0994323 .1825778
_cons | -1.567939 .7245511 -2.16 0.031 -2.991339 -.144538
------------------------------------------------------------------------------
*b1 measures the average wage difference between men and women with the same level of educ,
exper and tenure.
reg wage female
Source | SS df MS Number of obs = 526
-------------+---------------------------------- F(1, 524) = 68.54
Model | 828.220467 1 828.220467 Prob > F = 0.0000
Residual | 6332.19382 524 12.0843394 R-squared = 0.1157
-------------+---------------------------------- Adj R-squared = 0.1140
Total | 7160.41429 525 13.6388844 Root MSE = 3.4763
------------------------------------------------------------------------------
wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | -2.51183 .3034092 -8.28 0.000 -3.107878 -1.915782
_cons | 7.099489 .2100082 33.81 0.000 6.686928 7.51205
------------------------------------------------------------------------------
*b0 is the average wage for men in the sample.
mean wage if female==0
Mean estimation Number of obs = 274
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
wage | 7.099489 .2513666 6.604626 7.594352
--------------------------------------------------------------
*b1 is the average wage difference between men and women, accounts no other factor
mean wage if female==1
Mean estimation Number of obs = 252
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
wage | 4.587659 .1593349 4.273855 4.901462
--------------------------------------------------------------
display 4.5877 - 7.0995
-2.5118
*Example7.2. Effect of computer ownership on collage GPA
u gpa1, clear
reg colGPA PC hsGPA ACT
Source | SS df MS Number of obs = 141
-------------+---------------------------------- F(3, 137) = 12.83
Model | 4.25741863 3 1.41913954 Prob > F = 0.0000
Residual | 15.1486808 137 .110574313 R-squared = 0.2194
-------------+---------------------------------- Adj R-squared = 0.2023
Total | 19.4060994 140 .138614996 Root MSE = .33253
------------------------------------------------------------------------------
colGPA | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
PC | .1573092 .0572875 2.75 0.007 .0440271 .2705913
hsGPA | .4472417 .0936475 4.78 0.000 .2620603 .632423
ACT | .008659 .0105342 0.82 0.413 -.0121717 .0294897
_cons | 1.26352 .3331255 3.79 0.000 .6047871 1.922253
------------------------------------------------------------------------------
test hsGPA ACT
( 1) hsGPA = 0
( 2) ACT = 0
F( 2, 137) = 14.86
Prob > F = 0.0000
reg colGPA PC
Source | SS df MS Number of obs = 141
-------------+---------------------------------- F(1, 139) = 7.31
Model | .970092892 1 .970092892 Prob > F = 0.0077
Residual | 18.4360066 139 .132633141 R-squared = 0.0500
-------------+---------------------------------- Adj R-squared = 0.0432
Total | 19.4060994 140 .138614996 Root MSE = .36419
------------------------------------------------------------------------------
colGPA | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
PC | .1695168 .0626805 2.70 0.008 .0455864 .2934472
_cons | 2.989412 .0395018 75.68 0.000 2.91131 3.067514
------------------------------------------------------------------------------
mean colGPA if PC==0
Mean estimation Number of obs = 85
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
colGPA | 2.989412 .0348676 2.920074 3.05875
--------------------------------------------------------------
mean colGPA if PC==1
Mean estimation Number of obs = 56
--------------------------------------------------------------
| Mean Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
colGPA | 3.158929 .0562795 3.046142 3.271715
--------------------------------------------------------------
display 3.158929 - 2.989412
169517
*Example7.3. Effect of Training Grants on hours of training
u jtrain, clear
reg hrsemp grant lsales lemploy if year==1988
Source | SS df MS Number of obs = 105
-------------+---------------------------------- F(3, 101) = 10.44
Model | 18622.7268 3 6207.57559 Prob > F = 0.0000
Residual | 60031.0921 101 594.367249 R-squared = 0.2368
-------------+---------------------------------- Adj R-squared = 0.2141
Total | 78653.8189 104 756.28672 Root MSE = 24.38
------------------------------------------------------------------------------
hrsemp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
grant | 26.2545 5.591765 4.70 0.000 15.16194 37.34705
lsales | -.9845809 3.539903 -0.28 0.781 -8.006797 6.037635
lemploy | -6.069871 3.882893 -1.56 0.121 -13.77249 1.632744
_cons | 46.66508 43.4121 1.07 0.285 -39.45284 132.783
------------------------------------------------------------------------------
*Example7.4. Housing price regression
u hprice1, clear
d llotsize lsqrft bdrm colonial
storage display value
variable name type format label variable label
------------------------------------------------------------------------------------------
llotsize float %9.0g log(lotsize)
lsqrft float %9.0g log(sqrft)
bdrms byte %9.0g number of bdrms
colonial byte %9.0g =1 if home is colonial style
reg lprice llotsize lsqrft bdrm colonial
Source | SS df MS Number of obs = 88
-------------+---------------------------------- F(4, 83) = 38.38
Model | 5.20397919 4 1.3009948 Prob > F = 0.0000
Residual | 2.81362433 83 .033899088 R-squared = 0.6491
-------------+---------------------------------- Adj R-squared = 0.6322
Total | 8.01760352 87 .092156362 Root MSE = .18412
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llotsize | .1678189 .0381807 4.40 0.000 .0918791 .2437587
lsqrft | .7071931 .092802 7.62 0.000 .5226138 .8917725
bdrms | .0268305 .0287236 0.93 0.353 -.0302995 .0839605
colonial | .0537962 .0447732 1.20 0.233 -.035256 .1428483
_cons | -1.349589 .651041 -2.07 0.041 -2.644483 -.0546947
------------------------------------------------------------------------------
*Example7.5. Hourly wage equation
u wage1, clear
reg lwage female educ exper* tenur*
Source | SS df MS Number of obs = 526
-------------+---------------------------------- F(6, 519) = 68.18
Model | 65.3791009 6 10.8965168 Prob > F = 0.0000
Residual | 82.9506505 519 .159827843 R-squared = 0.4408
-------------+---------------------------------- Adj R-squared = 0.4343
Total | 148.329751 525 .28253286 Root MSE = .39978
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | -.296511 .0358055 -8.28 0.000 -.3668524 -.2261696
educ | .0801967 .0067573 11.87 0.000 .0669217 .0934716
exper | .0294324 .0049752 5.92 0.000 .0196585 .0392063
expersq | -.0005827 .0001073 -5.43 0.000 -.0007935 -.0003719
tenure | .0317139 .0068452 4.63 0.000 .0182663 .0451616
tenursq | -.0005852 .0002347 -2.49 0.013 -.0010463 -.0001241
_cons | .416691 .0989279 4.21 0.000 .2223425 .6110394
------------------------------------------------------------------------------
display exp(-.297) -1
-.25695599
*Example7.6. Hourly wage equation
u wage1, clear
g marrmale = (female==0 & married==1)
g marrfem = (female==1 & married==1)
g singfem = (female==1 & married==0)
g singmen = (female==0 & married==0)
reg lwage marrmale marrfem singfem educ exper* tenur*
Source | SS df MS Number of obs = 526
-------------+---------------------------------- F(8, 517) = 55.25
Model | 68.3617623 8 8.54522029 Prob > F = 0.0000
Residual | 79.9679891 517 .154676961 R-squared = 0.4609
-------------+---------------------------------- Adj R-squared = 0.4525
Total | 148.329751 525 .28253286 Root MSE = .39329
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
marrmale | .2126757 .0553572 3.84 0.000 .103923 .3214284
marrfem | -.1982676 .0578355 -3.43 0.001 -.311889 -.0846462
singfem | -.1103502 .0557421 -1.98 0.048 -.219859 -.0008414
educ | .0789103 .0066945 11.79 0.000 .0657585 .092062
exper | .0268006 .0052428 5.11 0.000 .0165007 .0371005
expersq | -.0005352 .0001104 -4.85 0.000 -.0007522 -.0003183
tenure | .0290875 .006762 4.30 0.000 .0158031 .0423719
tenursq | -.0005331 .0002312 -2.31 0.022 -.0009874 -.0000789
_cons | .3213781 .100009 3.21 0.001 .1249041 .5178521
------------------------------------------------------------------------------
reg lwage marrmale singmen singfem educ exper* tenur*
Source | SS df MS Number of obs = 526
-------------+---------------------------------- F(8, 517) = 55.25
Model | 68.3617623 8 8.54522029 Prob > F = 0.0000
Residual | 79.9679891 517 .154676961 R-squared = 0.4609
-------------+---------------------------------- Adj R-squared = 0.4525
Total | 148.329751 525 .28253286 Root MSE = .39329
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
marrmale | .4109433 .0457709 8.98 0.000 .3210234 .5008631
singmen | .1982676 .0578355 3.43 0.001 .0846462 .311889
singfem | .0879174 .0523481 1.68 0.094 -.0149238 .1907586
educ | .0789103 .0066945 11.79 0.000 .0657585 .092062
exper | .0268006 .0052428 5.11 0.000 .0165007 .0371005
expersq | -.0005352 .0001104 -4.85 0.000 -.0007522 -.0003183
tenure | .0290875 .006762 4.30 0.000 .0158031 .0423719
tenursq | -.0005331 .0002312 -2.31 0.022 -.0009874 -.0000789
_cons | .1231105 .1057937 1.16 0.245 -.0847279 .3309488
------------------------------------------------------------------------------
*Example7.7. Effects of physical attractiveness on wage
use beauty, clear
(Written by R. )
/* if the file is not available in your folder, export it from R using the following script
(assuming you have R installed in your PC, otherwise you may skip this):
install.packages("wooldridge")
library(wooldridge)
data("beauty")
require(foreign)
write.dta(beauty, "YOUR CURRENT DIRECTORY/beauty.dta")
*/
reg lwage belavg abvavg educ exper* union married black south good if female==0
Source | SS df MS Number of obs = 824
-------------+---------------------------------- F(10, 813) = 27.82
Model | 61.226435 10 6.1226435 Prob > F = 0.0000
Residual | 178.934819 813 .220092028 R-squared = 0.2549
-------------+---------------------------------- Adj R-squared = 0.2458
Total | 240.161253 823 .291811973 Root MSE = .46914
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
belavg | -.1647273 .0529083 -3.11 0.002 -.2685803 -.0608742
abvavg | -.0249691 .0377393 -0.66 0.508 -.0990471 .0491089
educ | .0606609 .0065805 9.22 0.000 .0477442 .0735776
exper | .046392 .0057854 8.02 0.000 .0350359 .057748
expersq | -.0007263 .0001234 -5.88 0.000 -.0009686 -.000484
union | .1485369 .036234 4.10 0.000 .0774136 .2196602
married | .0643684 .0442547 1.45 0.146 -.0224985 .1512353
black | -.2569891 .0756565 -3.40 0.001 -.4054942 -.1084839
south | .0852807 .042827 1.99 0.047 .0012161 .1693452
goodhlth | .0011008 .0701752 0.02 0.987 -.1366451 .1388467
_cons | .4777672 .120344 3.97 0.000 .2415456 .7139888
------------------------------------------------------------------------------
reg lwage belavg abvavg educ exper* union married black south good if female==1
Source | SS df MS Number of obs = 436
-------------+---------------------------------- F(10, 425) = 16.40
Model | 33.3026496 10 3.33026496 Prob > F = 0.0000
Residual | 86.3098141 425 .203081915 R-squared = 0.2784
-------------+---------------------------------- Adj R-squared = 0.2614
Total | 119.612464 435 .274971181 Root MSE = .45065
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
belavg | -.1141864 .066479 -1.72 0.087 -.2448548 .0164821
abvavg | .0685726 .0490607 1.40 0.163 -.0278592 .1650045
educ | .0756932 .0089025 8.50 0.000 .0581947 .0931917
exper | .0294842 .0071753 4.11 0.000 .0153807 .0435877
expersq | -.0004941 .0001756 -2.81 0.005 -.0008393 -.0001489
union | .2927388 .053905 5.43 0.000 .1867852 .3986925
married | -.0622614 .044332 -1.40 0.161 -.1493986 .0248759
black | .1437631 .0688923 2.09 0.038 .0083511 .2791751
south | .008481 .0595609 0.14 0.887 -.1085897 .1255516
goodhlth | .1130952 .081308 1.39 0.165 -.0467206 .272911
_cons | -.077281 .1441897 -0.54 0.592 -.3606948 .2061328
------------------------------------------------------------------------------
*Example7.8. Effects of law school rankings on starting salaries
u lawsch85, clear
g r61_100= (rank>=61 & rank<=100)
reg lsalary top10 r11_25 r26_40 r41_60 r61_100 LSAT GPA llibvol lcost
Source | SS df MS Number of obs = 136
-------------+---------------------------------- F(9, 126) = 143.20
Model | 9.45224101 9 1.050249 Prob > F = 0.0000
Residual | .924110799 126 .007334213 R-squared = 0.9109
-------------+---------------------------------- Adj R-squared = 0.9046
Total | 10.3763518 135 .076861865 Root MSE = .08564
------------------------------------------------------------------------------
lsalary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
top10 | .6995659 .053492 13.08 0.000 .5937069 .8054249
r11_25 | .5935433 .03944 15.05 0.000 .5154926 .671594
r26_40 | .3750763 .0340812 11.01 0.000 .3076305 .442522
r41_60 | .262819 .0279621 9.40 0.000 .2074829 .3181551
r61_100 | .1315949 .0210419 6.25 0.000 .0899537 .1732361
LSAT | .0056909 .003063 1.86 0.066 -.0003707 .0117525
GPA | .0137257 .0741919 0.19 0.854 -.1330979 .1605494
llibvol | .0363619 .0260165 1.40 0.165 -.015124 .0878478
lcost | .0008411 .025136 0.03 0.973 -.0489024 .0505846
_cons | 9.165294 .4114243 22.28 0.000 8.351098 9.979491
------------------------------------------------------------------------------
test LSAT GPA llibvol lcost
( 1) LSAT = 0
( 2) GPA = 0
( 3) llibvol = 0
( 4) lcost = 0
F( 4, 126) = 2.39
Prob > F = 0.0547
display exp(0.6996)-1
1.0129474
*Example7.9. Effects of computer usage on wages
*No data. Link to original article - Kruger 1995, https://www.nber.org/papers/w3858.pdf
*Example7.10. Log hourly wage equation
u wage1, clear
reg lwage c.female##c.educ exper* tenur*
Source | SS df MS Number of obs = 526
-------------+---------------------------------- F(7, 518) = 58.37
Model | 65.4081534 7 9.34402192 Prob > F = 0.0000
Residual | 82.921598 518 .160080305 R-squared = 0.4410
-------------+---------------------------------- Adj R-squared = 0.4334
Total | 148.329751 525 .28253286 Root MSE = .4001
---------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
----------------+----------------------------------------------------------------
female | -.2267886 .1675394 -1.35 0.176 -.5559289 .1023517
educ | .0823692 .0084699 9.72 0.000 .0657296 .0990088
c.female#c.educ | -.0055645 .0130618 -0.43 0.670 -.0312252 .0200962
exper | .0293366 .0049842 5.89 0.000 .019545 .0391283
expersq | -.0005804 .0001075 -5.40 0.000 -.0007916 -.0003691
tenure | .0318967 .006864 4.65 0.000 .018412 .0453814
tenursq | -.00059 .0002352 -2.51 0.012 -.001052 -.000128
_cons | .388806 .1186871 3.28 0.001 .1556388 .6219732
---------------------------------------------------------------------------------
test female c.female#c.educ
( 1) female = 0
( 2) c.female#c.educ = 0
F( 2, 518) = 34.33
Prob > F = 0.0000
*Example7.11. Effects of race on baseball player salaries
u mlb1, clear
reg lsalary years gamesyr bavg hrunsyr rbisyr runsyr fldperc allstar black hispan c.blac
> k#c.percblck c.hispan#c.perchisp if percblck !=.
Source | SS df MS Number of obs = 330
-------------+---------------------------------- F(12, 317) = 46.48
Model | 283.782162 12 23.6485135 Prob > F = 0.0000
Residual | 161.2793 317 .508767508 R-squared = 0.6376
-------------+---------------------------------- Adj R-squared = 0.6239
Total | 445.061462 329 1.3527704 Root MSE = .71328
-------------------------------------------------------------------------------------
lsalary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
years | .0673458 .0128915 5.22 0.000 .0419822 .0927094
gamesyr | .0088778 .0033837 2.62 0.009 .0022205 .0155352
bavg | .0009451 .0015133 0.62 0.533 -.0020322 .0039225
hrunsyr | .0146206 .0164522 0.89 0.375 -.0177488 .04699
rbisyr | .0044938 .007575 0.59 0.553 -.0104098 .0193974
runsyr | .0072029 .0045671 1.58 0.116 -.0017827 .0161885
fldperc | .0010865 .0021195 0.51 0.609 -.0030835 .0052566
allstar | .0075307 .0028735 2.62 0.009 .0018771 .0131842
black | -.198008 .1254968 -1.58 0.116 -.4449199 .0489038
hispan | -.1900089 .1530902 -1.24 0.215 -.4912101 .1111923
c.black#c.percblck | .0124513 .0049628 2.51 0.013 .0026872 .0222154
c.hispan#c.perchisp | .0200863 .0097933 2.05 0.041 .0008182 .0393543
_cons | 10.34368 2.182538 4.74 0.000 6.049594 14.63777
-------------------------------------------------------------------------------------
test black hispan c.black#c.percblck c.hispan#c.perchisp
( 1) black = 0
( 2) hispan = 0
( 3) c.black#c.percblck = 0
( 4) c.hispan#c.perchisp = 0
F( 4, 317) = 2.65
Prob > F = 0.0335
reg lsalary years gamesyr bavg hrunsyr rbisyr runsyr fldperc allstar if percblck !=.
Source | SS df MS Number of obs = 330
-------------+---------------------------------- F(8, 321) = 67.02
Model | 278.393524 8 34.7991905 Prob > F = 0.0000
Residual | 166.667938 321 .51921476 R-squared = 0.6255
-------------+---------------------------------- Adj R-squared = 0.6162
Total | 445.061462 329 1.3527704 Root MSE = .72057
------------------------------------------------------------------------------
lsalary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
years | .0672522 .0129735 5.18 0.000 .0417284 .092776
gamesyr | .008581 .0033684 2.55 0.011 .0019541 .0152078
bavg | .000654 .0015022 0.44 0.664 -.0023014 .0036095
hrunsyr | .0140303 .016539 0.85 0.397 -.0185083 .0465689
rbisyr | .004629 .0076231 0.61 0.544 -.0103684 .0196265
runsyr | .0084125 .004502 1.87 0.063 -.0004446 .0172695
fldperc | .0008579 .0021373 0.40 0.688 -.003347 .0050629
allstar | .0068969 .002886 2.39 0.017 .0012189 .0125748
_cons | 10.63671 2.198683 4.84 0.000 6.31106 14.96236
------------------------------------------------------------------------------
*Equation [7.22]
u gpa3, clear
reg cumgpa c.female##c.sat hsperc c.female#c.hsperc tothrs c.femal#c.tothrs if spring==1
Source | SS df MS Number of obs = 366
-------------+---------------------------------- F(7, 358) = 34.95
Model | 53.5391808 7 7.6484544 Prob > F = 0.0000
Residual | 78.3545052 358 .218867333 R-squared = 0.4059
-------------+---------------------------------- Adj R-squared = 0.3943
Total | 131.893686 365 .361352564 Root MSE = .46783
-----------------------------------------------------------------------------------
cumgpa | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------+----------------------------------------------------------------
female | -.3534862 .4105293 -0.86 0.390 -1.160838 .4538659
sat | .0010516 .0001811 5.81 0.000 .0006955 .0014078
c.female#c.sat | .0007506 .0003852 1.95 0.052 -6.88e-06 .0015081
hsperc | -.0084516 .0013704 -6.17 0.000 -.0111465 -.0057566
c.female#c.hsperc | -.0005498 .0031617 -0.17 0.862 -.0067676 .0056681
tothrs | .0023441 .0008624 2.72 0.007 .0006482 .0040401
c.female#c.tothrs | -.0001158 .0016277 -0.07 0.943 -.0033169 .0030852
_cons | 1.480812 .2073336 7.14 0.000 1.073067 1.888557
-----------------------------------------------------------------------------------
test c.female#c.sat c.female#c.hsperc c.femal#c.tothrs
( 1) c.female#c.sat = 0
( 2) c.female#c.hsperc = 0
( 3) c.female#c.tothrs = 0
F( 3, 358) = 1.53
Prob > F = 0.2054
*Equation [7.25]
reg cumgpa female sat hsperc tothrs if spring==1
Source | SS df MS Number of obs = 366
-------------+---------------------------------- F(4, 361) = 59.74
Model | 52.5320205 4 13.1330051 Prob > F = 0.0000
Residual | 79.3616656 361 .219838409 R-squared = 0.3983
-------------+---------------------------------- Adj R-squared = 0.3916
Total | 131.893686 365 .361352564 Root MSE = .46887
------------------------------------------------------------------------------
cumgpa | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | .3100975 .0586128 5.29 0.000 .1948321 .4253629
sat | .0012144 .0001591 7.63 0.000 .0009016 .0015272
hsperc | -.0084413 .0012343 -6.84 0.000 -.0108687 -.0060139
tothrs | .0024638 .0007291 3.38 0.001 .00103 .0038976
_cons | 1.328541 .1798275 7.39 0.000 .9748996 1.682182
------------------------------------------------------------------------------
*Equation [7.29]
u mroz, clear
reg inlf nwifeinc educ exper* age kidslt6 kidsge6
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(7, 745) = 38.22
Model | 48.8080578 7 6.97257969 Prob > F = 0.0000
Residual | 135.919698 745 .182442547 R-squared = 0.2642
-------------+---------------------------------- Adj R-squared = 0.2573
Total | 184.727756 752 .245648611 Root MSE = .42713
------------------------------------------------------------------------------
inlf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0034052 .0014485 -2.35 0.019 -.0062488 -.0005616
educ | .0379953 .007376 5.15 0.000 .023515 .0524756
exper | .0394924 .0056727 6.96 0.000 .0283561 .0506287
expersq | -.0005963 .0001848 -3.23 0.001 -.0009591 -.0002335
age | -.0160908 .0024847 -6.48 0.000 -.0209686 -.011213
kidslt6 | -.2618105 .0335058 -7.81 0.000 -.3275875 -.1960335
kidsge6 | .0130122 .013196 0.99 0.324 -.0128935 .0389179
_cons | .5855192 .154178 3.80 0.000 .2828442 .8881943
------------------------------------------------------------------------------
*Example7.12. A linear probability model of arrest
u crime1, clear
g arr86 = (narr>0)
reg arr86 pcnv avgsen tottime ptime qemp
Source | SS df MS Number of obs = 2,725
-------------+---------------------------------- F(5, 2719) = 27.03
Model | 25.8452455 5 5.16904909 Prob > F = 0.0000
Residual | 519.971268 2,719 .191236215 R-squared = 0.0474
-------------+---------------------------------- Adj R-squared = 0.0456
Total | 545.816514 2,724 .20037317 Root MSE = .43731
------------------------------------------------------------------------------
arr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.1624448 .0212368 -7.65 0.000 -.2040866 -.120803
avgsen | .0061127 .006452 0.95 0.344 -.0065385 .018764
tottime | -.0022616 .0049781 -0.45 0.650 -.0120229 .0074997
ptime86 | -.0219664 .0046349 -4.74 0.000 -.0310547 -.0128781
qemp86 | -.0428294 .0054046 -7.92 0.000 -.0534268 -.0322319
_cons | .4406154 .0172329 25.57 0.000 .4068246 .4744063
------------------------------------------------------------------------------
test avgsen tottime
( 1) avgsen = 0
( 2) tottime = 0
F( 2, 2719) = 1.06
Prob > F = 0.3467
*Equation [7.32]
reg arr86 pcnv avgsen tottime ptime qemp black hispan
Source | SS df MS Number of obs = 2,725
-------------+---------------------------------- F(7, 2717) = 28.41
Model | 37.2205275 7 5.31721822 Prob > F = 0.0000
Residual | 508.595986 2,717 .187190278 R-squared = 0.0682
-------------+---------------------------------- Adj R-squared = 0.0658
Total | 545.816514 2,724 .20037317 Root MSE = .43265
------------------------------------------------------------------------------
arr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.152062 .0210655 -7.22 0.000 -.193368 -.1107561
avgsen | .0046191 .0063888 0.72 0.470 -.0079083 .0171465
tottime | -.0025619 .0049259 -0.52 0.603 -.0122207 .0070969
ptime86 | -.0236954 .0045948 -5.16 0.000 -.032705 -.0146858
qemp86 | -.0384737 .0054016 -7.12 0.000 -.0490653 -.0278821
black | .1697631 .0236738 7.17 0.000 .1233426 .2161836
hispan | .0961866 .0207105 4.64 0.000 .0555766 .1367965
_cons | .3804283 .0187272 20.31 0.000 .3437073 .4171493
------------------------------------------------------------------------------
*Equation [7.33]
u jtrain, clear
reg lscrap grant lsales lemploy if year==1988
Source | SS df MS Number of obs = 50
-------------+---------------------------------- F(3, 46) = 1.18
Model | 6.8054029 3 2.26846763 Prob > F = 0.3270
Residual | 88.2852083 46 1.91924366 R-squared = 0.0716
-------------+---------------------------------- Adj R-squared = 0.0110
Total | 95.0906112 49 1.94062472 Root MSE = 1.3854
------------------------------------------------------------------------------
lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
grant | -.0517781 .4312869 -0.12 0.905 -.9199137 .8163574
lsales | -.4548425 .3733152 -1.22 0.229 -1.206287 .2966021
lemploy | .6394289 .3651366 1.75 0.087 -.095553 1.374411
_cons | 4.986779 4.655588 1.07 0.290 -4.384433 14.35799
------------------------------------------------------------------------------
*Equation [7.35] & [7.37]
u fertil2, clear
reg children age educ
Source | SS df MS Number of obs = 4,361
-------------+---------------------------------- F(2, 4358) = 2767.70
Model | 12044.5522 2 6022.27608 Prob > F = 0.0000
Residual | 9482.62417 4,358 2.17591193 R-squared = 0.5595
-------------+---------------------------------- Adj R-squared = 0.5593
Total | 21527.1763 4,360 4.93742577 Root MSE = 1.4751
------------------------------------------------------------------------------
children | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | .1748306 .0027051 64.63 0.000 .1695273 .1801339
educ | -.0899397 .0059826 -15.03 0.000 -.1016686 -.0782108
_cons | -1.99675 .0939688 -21.25 0.000 -2.180976 -1.812523
------------------------------------------------------------------------------
reg children age educ electric
Source | SS df MS Number of obs = 4,358
-------------+---------------------------------- F(3, 4354) = 1862.83
Model | 12090.395 3 4030.13167 Prob > F = 0.0000
Residual | 9419.6371 4,354 2.16344444 R-squared = 0.5621
-------------+---------------------------------- Adj R-squared = 0.5618
Total | 21510.0321 4,357 4.93689055 Root MSE = 1.4709
------------------------------------------------------------------------------
children | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | .1769991 .0027291 64.86 0.000 .1716486 .1823496
educ | -.0787507 .0063195 -12.46 0.000 -.09114 -.0663614
electric | -.3617579 .0680316 -5.32 0.000 -.4951345 -.2283813
_cons | -2.071091 .0947413 -21.86 0.000 -2.256832 -1.88535
------------------------------------------------------------------------------
log close
name: SN
log: ~Wooldridge\intro-econx\iexample7.smcl
log type: smcl
closed on: 9 Jan 2019, 00:27:17
------------------------------------------------------------------------------------------