## INTRODUCTORY ECONOMETRICS – REPLICATING EXAMPLES

### Chapter 4 – Examples

```-----------------------------------------------
name:  SN
log:  Wooldridge\intro-econx\iexample4.smcl
log type:  smcl
opened on:   6 Jan 2019, 17:17:19

. **********************************************
. * Solomon Negash - Replicating Examples
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6ed.
. * STATA Program, version 15.1.

. * Chapter 4  - Multiple Regression Analysis: Inference
. * Computer Exercises (Examples)
. ******************** SETUP *********************

*example4.1. Wage equation
. u wage1.dta, clear
. reg lwage educ exper tenure

Source |       SS           df       MS      Number of obs   =       526
-------------+----------------------------------   F(3, 522)       =     80.39
Model |  46.8741776         3  15.6247259   Prob > F        =    0.0000
Residual |  101.455574       522  .194359337   R-squared       =    0.3160
Total |  148.329751       525   .28253286   Root MSE        =    .44086

------------------------------------------------------------------------------
lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ |    .092029   .0073299    12.56   0.000     .0776292    .1064288
exper |   .0041211   .0017233     2.39   0.017     .0007357    .0075065
tenure |   .0220672   .0030936     7.13   0.000     .0159897    .0281448
_cons |   .2843595   .1041904     2.73   0.007     .0796756    .4890435
------------------------------------------------------------------------------

*example4.2. Student performance
. u meap93.dta, clear
. *Lin-lin model
. eststo: reg math10 totcomp staff enroll

Source |       SS           df       MS      Number of obs   =       408
-------------+----------------------------------   F(3, 404)       =      7.70
Model |  2422.93434         3  807.644779   Prob > F        =    0.0001
Residual |  42394.2462       404  104.936253   R-squared       =    0.0541
Total |  44817.1805       407  110.115923   Root MSE        =    10.244

------------------------------------------------------------------------------
math10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
totcomp |   .0004586   .0001004     4.57   0.000     .0002613    .0006559
staff |   .0479199    .039814     1.20   0.229    -.0303487    .1261884
enroll |  -.0001976   .0002152    -0.92   0.359    -.0006207    .0002255
_cons |   2.274021   6.113794     0.37   0.710    -9.744801    14.29284
------------------------------------------------------------------------------
(est1 stored)
. *Lin-log model
. eststo: reg math10 ltotcomp lstaff lenroll

Source |       SS           df       MS      Number of obs   =       408
-------------+----------------------------------   F(3, 404)       =      9.42
Model |  2930.03493         3  976.678311   Prob > F        =    0.0000
Residual |  41887.1456       404  103.681053   R-squared       =    0.0654
Total |  44817.1805       407  110.115923   Root MSE        =    10.182

------------------------------------------------------------------------------
math10 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ltotcomp |     21.155   4.055549     5.22   0.000     13.18238    29.12761
lstaff |   3.980018    4.18966     0.95   0.343    -4.256239    12.21628
lenroll |  -1.268046    .693204    -1.83   0.068    -2.630784    .0946912
_cons |  -207.6648   48.70313    -4.26   0.000     -303.408   -111.9216
------------------------------------------------------------------------------
(est2 stored)

. estout *, cells(b(star fmt(3)) se(par fmt(2))) stats(r2_a N, fmt(%9.3f %9.0g) label
> s(R-squared))   legend label collabels(none) varlabels(_cons Constant)

----------------------------------------------------
est1            est2
----------------------------------------------------
salary + benefits           0.000***
(0.00)
staff per 1000 stu~s        0.048
(0.04)
school enrollment          -0.000
(0.00)
log(totcomp)                               21.155***
(4.06)
log(staff)                                  3.980
(4.19)
log(enroll)                                -1.268
(0.69)
Constant                    2.274        -207.665***
(6.11)         (48.70)
----------------------------------------------------
R-squared                   0.047           0.058
N                             408             408
----------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001
. est clear

*example4.3. Collage GPA
. u gpa1.dta, clear
. reg colGPA hsGPA ACT skipped

Source |       SS           df       MS      Number of obs   =       141
-------------+----------------------------------   F(3, 137)       =     13.92
Model |  4.53313314         3  1.51104438   Prob > F        =    0.0000
Residual |  14.8729663       137  .108561798   R-squared       =    0.2336
Total |  19.4060994       140  .138614996   Root MSE        =    .32949

------------------------------------------------------------------------------
colGPA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hsGPA |   .4118162   .0936742     4.40   0.000     .2265819    .5970505
ACT |   .0147202   .0105649     1.39   0.166    -.0061711    .0356115
skipped |  -.0831131   .0259985    -3.20   0.002    -.1345234   -.0317028
_cons |   1.389554   .3315535     4.19   0.000     .7339295    2.045178
------------------------------------------------------------------------------

*example4.4. Campus crime & enrollment
. u campus.dta, clear
. reg lcrime lenroll

Source |       SS           df       MS      Number of obs   =        97
-------------+----------------------------------   F(1, 95)        =    133.79
Model |  107.083654         1  107.083654   Prob > F        =    0.0000
Residual |  76.0358244        95  .800377098   R-squared       =    0.5848
Total |  183.119479        96  1.90749457   Root MSE        =    .89464

------------------------------------------------------------------------------
lcrime |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lenroll |    1.26976    .109776    11.57   0.000     1.051827    1.487693
_cons |   -6.63137    1.03354    -6.42   0.000    -8.683206   -4.579533
------------------------------------------------------------------------------

*example4.5. Housing prices
. u hprice2.dta, clear
. g ldist=ln(dist)
. reg  lprice lnox ldist rooms stratio

Source |       SS           df       MS      Number of obs   =       506
-------------+----------------------------------   F(4, 501)       =    175.86
Model |  49.3987586         4  12.3496897   Prob > F        =    0.0000
Residual |  35.1834663       501   .07022648   R-squared       =    0.5840
Total |   84.582225       505  .167489554   Root MSE        =      .265

------------------------------------------------------------------------------
lprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnox |  -.9535388   .1167417    -8.17   0.000    -1.182902   -.7241751
ldist |  -.1343395   .0431032    -3.12   0.002    -.2190247   -.0496542
rooms |   .2545271   .0185303    13.74   0.000     .2181203    .2909338
stratio |  -.0524511   .0058971    -8.89   0.000    -.0640372    -.040865
_cons |   11.08386   .3181113    34.84   0.000     10.45887    11.70886
------------------------------------------------------------------------------

*example4.6. Participation rates in 401k plans
. u 401k.dta, clear
. reg prate mrate age totemp

Source |       SS           df       MS      Number of obs   =     1,534
-------------+----------------------------------   F(3, 1530)      =     56.38
Model |  42642.5383         3  14214.1794   Prob > F        =    0.0000
Residual |  385743.001     1,530  252.119609   R-squared       =    0.0995
Total |  428385.539     1,533  279.442622   Root MSE        =    15.878

------------------------------------------------------------------------------
prate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
mrate |   5.442221    .524419    10.38   0.000     4.413565    6.470878
age |   .2691979   .0451449     5.96   0.000     .1806455    .3577503
totemp |  -.0001291   .0000367    -3.52   0.000     -.000201   -.0000572
_cons |   80.29405   .7777274   103.24   0.000     78.76853    81.81958
------------------------------------------------------------------------------

*example4.7. Job training (only for the year 1987 and for nonunionized firms)
. u jtrain.dta, clear
. d year union

storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------
year            int     %9.0g                 1987, 1988, or 1989
union           byte    %9.0g                 =1 if unionized

. reg lscrap hrsemp lsales lemploy if year==1987 & union==0

Source |       SS           df       MS      Number of obs   =        29
-------------+----------------------------------   F(3, 25)        =      2.97
Model |  16.8426986         3  5.61423287   Prob > F        =    0.0513
Residual |  47.3369125        25   1.8934765   R-squared       =    0.2624
Total |  64.1796111        28  2.29212897   Root MSE        =     1.376

------------------------------------------------------------------------------
lscrap |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hrsemp |  -.0292689   .0228048    -1.28   0.211    -.0762364    .0176985
lsales |  -.9620269   .4525181    -2.13   0.044    -1.894005   -.0300484
lemploy |   .7614704   .4074328     1.87   0.073    -.0776532    1.600594
_cons |   12.45837    5.68677     2.19   0.038      .746249    24.17049
------------------------------------------------------------------------------

*example4.8.
. u rdchem.dta, clear
. reg lrd lsales profmarg

Source |       SS           df       MS      Number of obs   =        32
-------------+----------------------------------   F(2, 29)        =    162.23
Model |  85.5967531         2  42.7983766   Prob > F        =    0.0000
Residual |  7.65051127        29  .263810733   R-squared       =    0.9180
Total |  93.2472644        31  3.00797627   Root MSE        =    .51363

------------------------------------------------------------------------------
lrd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsales |    1.08422    .060195    18.01   0.000     .9611073    1.207333
profmarg |   .0216557   .0127826     1.69   0.101    -.0044877    .0477991
_cons |  -4.378273   .4680185    -9.35   0.000    -5.335479   -3.421068
------------------------------------------------------------------------------

*example4.9. Parent's education on birth weight
. u bwght.dta, clear
. reg bwght cigs parity faminc motheduc fatheduc

Source |       SS           df       MS      Number of obs   =     1,191
-------------+----------------------------------   F(5, 1185)      =      9.55
Model |  18705.5567         5  3741.11135   Prob > F        =    0.0000
Residual |  464041.135     1,185  391.595895   R-squared       =    0.0387
Total |  482746.692     1,190  405.669489   Root MSE        =    19.789

------------------------------------------------------------------------------
bwght |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
cigs |  -.5959362   .1103479    -5.40   0.000    -.8124352   -.3794373
parity |   1.787603   .6594055     2.71   0.007     .4938709    3.081336
faminc |   .0560414   .0365616     1.53   0.126    -.0156913    .1277742
motheduc |  -.3704503   .3198551    -1.16   0.247    -.9979957    .2570951
fatheduc |   .4723944   .2826433     1.67   0.095    -.0821426    1.026931
_cons |   114.5243   3.728453    30.72   0.000     107.2092    121.8394
------------------------------------------------------------------------------

. test motheduc fatheduc

( 1)  motheduc = 0
( 2)  fatheduc = 0

F(  2,  1185) =    1.44
Prob > F =    0.2380

. reg bwght cigs parity faminc

Source |       SS           df       MS      Number of obs   =     1,388
-------------+----------------------------------   F(3, 1384)      =     16.63
Model |  19996.5211         3  6665.50703   Prob > F        =    0.0000
Residual |  554615.199     1,384  400.733525   R-squared       =    0.0348
Total |   574611.72     1,387  414.283864   Root MSE        =    20.018

------------------------------------------------------------------------------
bwght |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
cigs |  -.4771537    .091518    -5.21   0.000    -.6566827   -.2976247
parity |   1.616372    .603955     2.68   0.008     .4316058    2.801138
faminc |   .0979201   .0291868     3.35   0.001      .040665    .1551752
_cons |   114.2143     1.4693    77.73   0.000     111.3321    117.0966
------------------------------------------------------------------------------

*Exaploring further 4.5.
. u attend, clear
. eststo: reg atndrte priGPA

Source |       SS           df       MS      Number of obs   =       680
-------------+----------------------------------   F(1, 678)       =    151.35
Model |  36008.3571         1  36008.3571   Prob > F        =    0.0000
Residual |  161308.968       678  237.918832   R-squared       =    0.1825
Total |  197317.325       679   290.59989   Root MSE        =    15.425

------------------------------------------------------------------------------
atndrte |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
priGPA |   13.36898   1.086703    12.30   0.000     11.23527    15.50268
_cons |   47.12702   2.872615    16.41   0.000     41.48673    52.76732
------------------------------------------------------------------------------
(est1 stored)

. eststo: reg atndrte priGPA ACT

Source |       SS           df       MS      Number of obs   =       680
-------------+----------------------------------   F(2, 677)       =    138.65
Model |  57336.7612         2  28668.3806   Prob > F        =    0.0000
Residual |  139980.564       677  206.765974   R-squared       =    0.2906
Total |  197317.325       679   290.59989   Root MSE        =    14.379

------------------------------------------------------------------------------
atndrte |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
priGPA |   17.26059   1.083103    15.94   0.000     15.13395    19.38724
ACT |  -1.716553    .169012   -10.16   0.000    -2.048404   -1.384702
_cons |    75.7004   3.884108    19.49   0.000     68.07406    83.32675
------------------------------------------------------------------------------
(est2 stored)

. estout *, cells(b(star fmt(3)) se(par fmt(2))) stats(r2_a N, fmt(%9.3f %9.0g) label
> s(R-squared))   legend label collabels(none) varlabels(_cons Constant)

----------------------------------------------------
est1            est2
----------------------------------------------------
cumulative GPA pri~m       13.369***       17.261***
(1.09)          (1.08)
ACT score                                  -1.717***
(0.17)
Constant                   47.127***       75.700***
(2.87)          (3.88)
----------------------------------------------------
R-squared                   0.181           0.288
N                             680             680
----------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001
. est clear

. u meap93.dta, clear
. d bensal
storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------
bensal          float   %9.0g                 benefits/salary
. eststo: qui reg lsalary bensal
(est1 stored)
. eststo: qui reg lsalary bensal lenrol lstaff
(est2 stored)
. eststo: qui reg lsalary bensal lenrol lstaff droprate gradrate
(est3 stored)
. estout *, cells(b(star fmt(3)) se(par fmt(2))) stats(r2_a N, fmt(%9.3f %9.0g) label
> s(R-squared))    varlabels(_cons Constant) ti("Compare to Table 4.1 on the textbook
> ")

Compare to Table 4.1 in the textbook
------------------------------------------------------------
est1            est2            est3
b/se            b/se            b/se
------------------------------------------------------------
bensal             -0.825***       -0.605***       -0.589***
(0.20)          (0.17)          (0.16)
lenroll                             0.087***        0.088***
(0.01)          (0.01)
lstaff                             -0.222***       -0.218***
(0.05)          (0.05)
droprate                                           -0.000
(0.00)
(0.00)
Constant           10.523***       10.844***       10.738***
(0.04)          (0.25)          (0.26)
------------------------------------------------------------
R-squared           0.038           0.348           0.353
N                     408             408             408
------------------------------------------------------------
. est clear
. log close
name:  SN
log:  Wooldridge\intro-econx\iexample4.smcl
log type:  smcl
closed on:   6 Jan 2019, 17:17:20
-----------------------------------------------------
```

4 replies
1. Henock Ayalew Tekalegn says:

Hello Solomon. I am new to Econometrics but trying to learn new areas everyday. I am teaching R Programming (Data Science) at the moment and the application seems helpful to what I am learning. Is there any way you could share the notes you are using, please?
Thank you,

• Solomon says:

Hi Henock. You can download the data from cengage.com to your desktop and set the folder as your working directory in stata. The output file that I posted here includes all the necessary info (comments, commands and results) to replicate the examples. Once you download the data and set your working directory, you can simply copy and paste the commands (the simple texts without asterisk) and execute it from the command line. You will get the same result. If you have no access to the 6th edition, you may find previous versions online.

There is also an alternative resource for people like you who are familiar with R and wishes to learn Econometrics using R. The book (Using R for Introductory Econometrics) has an online version which is available for free and has extra material with examples and replication exercises.

Best,

2. Elu says:

Hi Solomon,
I appreciate your effort and I found it very interesting. I will find a way to explore the material further and I will also try to use Python for the analysis. Anyways, I will let you know my findings.
Melkam Gena
Best