Chapter 4 Single-Equation Linear Model and OLS Estimation
Examples
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name: SN
log: myReplications\iiexample4
log type: smcl
opened on: 5 Jun 2019, 14:06:09
. **********************************************
. * Solomon Negash - Replicating Examples
. * Wooldridge (2010). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 15.1.
. * Chapter 4 - Single-Equation and OLS Estimation
. * Computer Exercises (Examples)
. **********************************************
. // Example4.1 Wage equation for married working women
. bcuse mroz, clear nodesc
. reg lwage exper expersq educ age kidslt6 kidsge6
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(6, 421) = 13.19
Model | 35.3398149 6 5.88996914 Prob > F = 0.0000
Residual | 187.987636 421 .446526452 R-squared = 0.1582
-------------+---------------------------------- Adj R-squared = 0.1462
Total | 223.327451 427 .523015108 Root MSE = .66823
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .039819 .013393 2.97 0.003 .0134936 .0661444
expersq | -.0007812 .0004022 -1.94 0.053 -.0015718 9.37e-06
educ | .107832 .0144021 7.49 0.000 .079523 .1361409
age | -.0014653 .0052925 -0.28 0.782 -.0118682 .0089377
kidslt6 | -.0607106 .0887626 -0.68 0.494 -.2351837 .1137625
kidsge6 | -.014591 .0278981 -0.52 0.601 -.069428 .0402459
_cons | -.4209079 .316905 -1.33 0.185 -1.043821 .2020052
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. test kidsge6 kidslt6 age
( 1) kidsge6 = 0
( 2) kidslt6 = 0
( 3) age = 0
F( 3, 421) = 0.24
Prob > F = 0.8705
. predict u, residual
(325 missing values generated)
. //LM1 page64
. reg lwage exper expersq educ
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(3, 424) = 26.29
Model | 35.0223023 3 11.6741008 Prob > F = 0.0000
Residual | 188.305149 424 .444115917 R-squared = 0.1568
-------------+---------------------------------- Adj R-squared = 0.1509
Total | 223.327451 427 .523015108 Root MSE = .66642
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0415665 .0131752 3.15 0.002 .0156697 .0674633
expersq | -.0008112 .0003932 -2.06 0.040 -.0015841 -.0000382
educ | .1074896 .0141465 7.60 0.000 .0796837 .1352956
_cons | -.5220407 .1986321 -2.63 0.009 -.9124668 -.1316145
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. predict u_r, residual
(325 missing values generated)
. reg u_r exper expersq educ age kidslt6 kidsge6
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(6, 421) = 0.12
Model | .317512527 6 .052918754 Prob > F = 0.9942
Residual | 187.987638 421 .446526456 R-squared = 0.0017
-------------+---------------------------------- Adj R-squared = -0.0125
Total | 188.305151 427 .440995669 Root MSE = .66823
------------------------------------------------------------------------------
u_r | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.0017475 .013393 -0.13 0.896 -.0280729 .0245779
expersq | .00003 .0004022 0.07 0.941 -.0007606 .0008206
educ | .0003423 .0144021 0.02 0.981 -.0279666 .0286512
age | -.0014653 .0052925 -0.28 0.782 -.0118682 .0089377
kidslt6 | -.0607106 .0887626 -0.68 0.494 -.2351837 .1137625
kidsge6 | -.014591 .0278981 -0.52 0.601 -.069428 .0402459
_cons | .1011327 .316905 0.32 0.750 -.5217804 .7240459
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. display "N*Rsquared =" e(r2)*e(N)
N*Rsquared =.72167628
. di chi2tail(3, e(r2)*e(N))
.86809401
. //LM2 page65 (example4.1 cont'd)
. foreach x of var age kidslt kidsg{
2. reg `x' exper* edu
3. predict r_`x', residual
4. gen ures`x'= u_r*r_`x'
5. }
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(3, 749) = 48.91
Model | 8027.34887 3 2675.78296 Prob > F = 0.0000
Residual | 40977.8224 749 54.7100433 R-squared = 0.1638
-------------+---------------------------------- Adj R-squared = 0.1605
Total | 49005.1713 752 65.1664512 Root MSE = 7.3966
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age | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.1424069 .096921 -1.47 0.142 -.332676 .0478621
expersq | .016711 .0031269 5.34 0.000 .0105724 .0228496
educ | -.4363879 .1192663 -3.66 0.000 -.670524 -.2022518
_cons | 46.43838 1.521431 30.52 0.000 43.4516 49.42515
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(325 missing values generated)
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(3, 749) = 13.85
Model | 10.8498698 3 3.61662327 Prob > F = 0.0000
Residual | 195.599001 749 .261146864 R-squared = 0.0526
-------------+---------------------------------- Adj R-squared = 0.0488
Total | 206.448871 752 .274533073 Root MSE = .51103
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kidslt6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.0145597 .0066962 -2.17 0.030 -.0277052 -.0014142
expersq | .0000493 .000216 0.23 0.819 -.0003748 .0004734
educ | .0282583 .00824 3.43 0.001 .0120821 .0444345
_cons | .0365076 .1051141 0.35 0.728 -.1698457 .2428609
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(325 missing values generated)
Source | SS df MS Number of obs = 753
-------------+---------------------------------- F(3, 749) = 26.14
Model | 124.144723 3 41.3815742 Prob > F = 0.0000
Residual | 1185.88981 749 1.58329747 R-squared = 0.0948
-------------+---------------------------------- Adj R-squared = 0.0911
Total | 1310.03453 752 1.74206719 Root MSE = 1.2583
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kidsge6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | -.0221468 .0164879 -1.34 0.180 -.0545148 .0102212
expersq | -.0009084 .0005319 -1.71 0.088 -.0019526 .0001359
educ | -.0264995 .0202892 -1.31 0.192 -.06633 .013331
_cons | 2.07601 .2588212 8.02 0.000 1.567909 2.584111
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(325 missing values generated)
. gen one=1
. reg one ures*, noc
Source | SS df MS Number of obs = 428
-------------+---------------------------------- F(3, 425) = 0.17
Model | .521512118 3 .173837373 Prob > F = 0.9147
Residual | 427.478488 425 1.00583174 R-squared = 0.0012
-------------+---------------------------------- Adj R-squared = -0.0058
Total | 428 428 1 Root MSE = 1.0029
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one | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uresage | -.0028426 .0109138 -0.26 0.795 -.0242943 .018609
ureskidslt6 | -.0940857 .1710924 -0.55 0.583 -.4303783 .2422068
ureskidsge6 | -.0266196 .059492 -0.45 0.655 -.1435548 .0903155
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. display "LM= N-SSRo =" e(N)-e(rss)
LM= N-SSRo =.52151212
. di chi2tail(3, e(N)-e(rss))
.9141405
. //Example4.2 NA
. //Example4.3 Using IQ as a Proxy for Ability
. bcuse nls80, clear nodesc
. reg lwage exper tenure married south urban black educ
Source | SS df MS Number of obs = 935
-------------+---------------------------------- F(7, 927) = 44.75
Model | 41.8377619 7 5.97682312 Prob > F = 0.0000
Residual | 123.818521 927 .133569063 R-squared = 0.2526
-------------+---------------------------------- Adj R-squared = 0.2469
Total | 165.656283 934 .177362188 Root MSE = .36547
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lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .014043 .0031852 4.41 0.000 .007792 .020294
tenure | .0117473 .002453 4.79 0.000 .0069333 .0165613
married | .1994171 .0390502 5.11 0.000 .1227801 .276054
south | -.0909036 .0262485 -3.46 0.001 -.142417 -.0393903
urban | .1839121 .0269583 6.82 0.000 .1310056 .2368185
black | -.1883499 .0376666 -5.00 0.000 -.2622717 -.1144281
educ | .0654307 .0062504 10.47 0.000 .0531642 .0776973
_cons | 5.395497 .113225 47.65 0.000 5.17329 5.617704
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. reg lwage exper tenure married south urban black educ iq
Source | SS df MS Number of obs = 935
-------------+---------------------------------- F(8, 926) = 41.27
Model | 43.5360162 8 5.44200202 Prob > F = 0.0000
Residual | 122.120267 926 .131879338 R-squared = 0.2628
-------------+---------------------------------- Adj R-squared = 0.2564
Total | 165.656283 934 .177362188 Root MSE = .36315
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0141458 .0031651 4.47 0.000 .0079342 .0203575
tenure | .0113951 .0024394 4.67 0.000 .0066077 .0161825
married | .1997644 .0388025 5.15 0.000 .1236134 .2759154
south | -.0801695 .0262529 -3.05 0.002 -.1316916 -.0286473
urban | .1819463 .0267929 6.79 0.000 .1293645 .2345281
black | -.1431253 .0394925 -3.62 0.000 -.2206304 -.0656202
educ | .0544106 .0069285 7.85 0.000 .0408133 .068008
iq | .0035591 .0009918 3.59 0.000 .0016127 .0055056
_cons | 5.176439 .1280006 40.44 0.000 4.925234 5.427644
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. //Example4.4 Effects of Job Training Grants on Worker Productivity *(JTRAIN.RAW)
. use jtrain1, clear
. regress lscrap grant if year==1988
Source | SS df MS Number of obs = 54
-------------+---------------------------------- F(1, 52) = 0.02
Model | .039451758 1 .039451758 Prob > F = 0.8895
Residual | 105.323208 52 2.02544631 R-squared = 0.0004
-------------+---------------------------------- Adj R-squared = -0.0188
Total | 105.36266 53 1.98797472 Root MSE = 1.4232
------------------------------------------------------------------------------
lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
grant | .0566004 .4055519 0.14 0.890 -.757199 .8703998
_cons | .408526 .2405616 1.70 0.095 -.0741962 .8912482
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. regress lscrap grant lscrap_1 if year==1988
Source | SS df MS Number of obs = 54
-------------+---------------------------------- F(2, 51) = 174.94
Model | 91.9584791 2 45.9792396 Prob > F = 0.0000
Residual | 13.4041809 51 .262827077 R-squared = 0.8728
-------------+---------------------------------- Adj R-squared = 0.8678
Total | 105.36266 53 1.98797472 Root MSE = .51267
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lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
grant | -.2539697 .1470311 -1.73 0.090 -.5491469 .0412076
lscrap_1 | .8311606 .0444444 18.70 0.000 .7419347 .9203865
_cons | .021237 .0890967 0.24 0.813 -.1576321 .2001061
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. //Example4.5
. bcuse nls80, clear nodesc
. reg lwage exper tenure married south urban black educ iq c.educ#c.iq
Source | SS df MS Number of obs = 935
-------------+---------------------------------- F(9, 925) = 36.76
Model | 43.6401231 9 4.84890256 Prob > F = 0.0000
Residual | 122.01616 925 .131909362 R-squared = 0.2634
-------------+---------------------------------- Adj R-squared = 0.2563
Total | 165.656283 934 .177362188 Root MSE = .36319
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exper | .0139072 .0031768 4.38 0.000 .0076725 .0201418
tenure | .0113929 .0024397 4.67 0.000 .0066049 .0161808
married | .2008658 .0388267 5.17 0.000 .1246671 .2770644
south | -.0802354 .026256 -3.06 0.002 -.1317637 -.0287072
urban | .1835758 .0268586 6.83 0.000 .1308649 .2362867
black | -.1466989 .0397013 -3.70 0.000 -.2246139 -.0687839
educ | .0184559 .0410608 0.45 0.653 -.0621272 .0990391
iq | -.0009418 .0051625 -0.18 0.855 -.0110734 .0091899
|
c.educ#c.iq | .0003399 .0003826 0.89 0.375 -.0004109 .0010907
|
_cons | 5.648248 .5462963 10.34 0.000 4.576124 6.720372
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. test iq c.educ#c.iq
( 1) iq = 0
( 2) c.educ#c.iq = 0
F( 2, 925) = 6.83
Prob > F = 0.0011
. //discripancy in coef & se of education
. //Example4.6 NA
. //Example4.7 NA
. //Example4.8 NA
. log close
name: SN
log: myReplications\iiexample4
log type: smcl
closed on: 5 Jun 2019, 14:06:12
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