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
-------------+----------------------------------   Adj R-squared   =    0.3121
       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
-------------+----------------------------------   Adj R-squared   =    0.0470
       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
-------------+----------------------------------   Adj R-squared   =    0.0584
       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
-------------+----------------------------------   Adj R-squared   =    0.2168
       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
-------------+----------------------------------   Adj R-squared   =    0.5804
       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
-------------+----------------------------------   Adj R-squared   =    0.5807
       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
-------------+----------------------------------   Adj R-squared   =    0.0978
       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
-------------+----------------------------------   Adj R-squared   =    0.1739
       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
-------------+----------------------------------   Adj R-squared   =    0.9123
       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
-------------+----------------------------------   Adj R-squared   =    0.0347
       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
-------------+----------------------------------   Adj R-squared   =    0.0327
       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
-------------+----------------------------------   Adj R-squared   =    0.1813
       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
-------------+----------------------------------   Adj R-squared   =    0.2885
       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

*example4.10. Salary-pension tradeoff for teachers
. 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)   
gradrate                                            0.001   
                                                   (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
    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,

    Reply
    • Solomon
      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
    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

    Reply

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