﻿ Chapter 13 Panel Data - Examples

## Chapter 13 Panel Data - Examples

```-------------------------------------------------------------------------------------
name:  SN
log:  ~Wooldridge\intro-econx\iexample13.smcl
log type:  smcl
opened on:  16 Jan 2019, 13:12:19

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

. * CHAPTER 13. Pooling Cross Sections across Time: Simple Panel Data Methods
. * Computer Exercises (Examples)
. ******************** SETUP *********************

. *Example 13.1. Women’s Fertility over Time
. u fertil1, clear
. reg kids educ age agesq black-y84

Source |       SS           df       MS      Number of obs   =     1,129
-------------+----------------------------------   F(17, 1111)     =      9.72
Model |  399.610888        17  23.5065228   Prob > F        =    0.0000
Residual |  2685.89841     1,111  2.41755033   R-squared       =    0.1295
-------------+----------------------------------   Adj R-squared   =    0.1162
Total |   3085.5093     1,128  2.73538059   Root MSE        =    1.5548

------------------------------------------------------------------------------
kids |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ |  -.1284268   .0183486    -7.00   0.000    -.1644286    -.092425
age |   .5321346   .1383863     3.85   0.000     .2606065    .8036626
agesq |   -.005804   .0015643    -3.71   0.000    -.0088733   -.0027347
black |   1.075658   .1735356     6.20   0.000     .7351631    1.416152
east |    .217324   .1327878     1.64   0.102    -.0432192    .4778672
northcen |    .363114   .1208969     3.00   0.003      .125902    .6003261
west |   .1976032   .1669134     1.18   0.237    -.1298978    .5251041
farm |  -.0525575     .14719    -0.36   0.721    -.3413592    .2362443
othrural |  -.1628537    .175442    -0.93   0.353    -.5070887    .1813814
town |   .0843532    .124531     0.68   0.498    -.1599893    .3286957
smcity |   .2118791    .160296     1.32   0.187    -.1026379    .5263961
y74 |   .2681825    .172716     1.55   0.121    -.0707039    .6070689
y76 |  -.0973795   .1790456    -0.54   0.587     -.448685    .2539261
y78 |  -.0686665   .1816837    -0.38   0.706    -.4251483    .2878154
y80 |  -.0713053   .1827707    -0.39   0.697      -.42992    .2873093
y82 |  -.5224842   .1724361    -3.03   0.003    -.8608214    -.184147
y84 |  -.5451661   .1745162    -3.12   0.002    -.8875846   -.2027477
_cons |  -7.742457   3.051767    -2.54   0.011    -13.73033   -1.754579
------------------------------------------------------------------------------
. test y74 y76 y78 y80 y82 y84
( 1)  y74 = 0
( 2)  y76 = 0
( 3)  y78 = 0
( 4)  y80 = 0
( 5)  y82 = 0
( 6)  y84 = 0
F(  6,  1111) =    5.87
Prob > F =    0.0000

. predict u, res
. g u2 = u^2
. qui reg u2 educ age agesq black-y84
. display e(N) * e(r2)
55.315374

. *Example 13.2. Changes in the Return to Education and the Gender Wage Gap
. u cps78_85, clear
. reg lwage y85 educ y85educ exper expersq union female y85fem

Source |       SS           df       MS      Number of obs   =     1,084
-------------+----------------------------------   F(8, 1075)      =     99.80
Model |  135.992074         8  16.9990092   Prob > F        =    0.0000
Residual |  183.099094     1,075  .170324738   R-squared       =    0.4262
-------------+----------------------------------   Adj R-squared   =    0.4219
Total |  319.091167     1,083   .29463635   Root MSE        =     .4127

------------------------------------------------------------------------------
lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
y85 |   .1178062   .1237817     0.95   0.341     -.125075    .3606874
educ |   .0747209   .0066764    11.19   0.000     .0616206    .0878212
y85educ |   .0184605   .0093542     1.97   0.049      .000106     .036815
exper |   .0295843   .0035673     8.29   0.000     .0225846     .036584
expersq |  -.0003994   .0000775    -5.15   0.000    -.0005516   -.0002473
union |   .2021319   .0302945     6.67   0.000     .1426888    .2615749
female |  -.3167086   .0366215    -8.65   0.000    -.3885663    -.244851
y85fem |    .085052    .051309     1.66   0.098    -.0156251     .185729
_cons |   .4589329   .0934485     4.91   0.000     .2755707     .642295
------------------------------------------------------------------------------
. display "Return to Education in 1978 is " _b[educ]*100 "%"
Return to Education in 1978 is 7.4720913%

. display "Return to Education in 1985 is " (_b[educ] + _b[y85educ])*100 "%"
Return to Education in 1985 is 9.3181445%

. *Example 13.3. Effect of a Garbage Incinerator’s Location on Housing Prices
. u KIELMC, clear
. reg rprice nearinc if year==1981

Source |       SS           df       MS      Number of obs   =       142
-------------+----------------------------------   F(1, 140)       =     27.73
Model |  2.7059e+10         1  2.7059e+10   Prob > F        =    0.0000
Residual |  1.3661e+11       140   975815048   R-squared       =    0.1653
-------------+----------------------------------   Adj R-squared   =    0.1594
Total |  1.6367e+11       141  1.1608e+09   Root MSE        =     31238

------------------------------------------------------------------------------
rprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc |  -30688.27   5827.709    -5.27   0.000    -42209.97   -19166.58
_cons |   101307.5   3093.027    32.75   0.000     95192.43    107422.6
------------------------------------------------------------------------------
. reg rprice nearinc if year==1978

Source |       SS           df       MS      Number of obs   =       179
-------------+----------------------------------   F(1, 177)       =     15.74
Model |  1.3636e+10         1  1.3636e+10   Prob > F        =    0.0001
Residual |  1.5332e+11       177   866239953   R-squared       =    0.0817
-------------+----------------------------------   Adj R-squared   =    0.0765
Total |  1.6696e+11       178   937979126   Root MSE        =     29432

------------------------------------------------------------------------------
rprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
nearinc |  -18824.37   4744.594    -3.97   0.000    -28187.62   -9461.117
_cons |   82517.23    2653.79    31.09   0.000     77280.09    87754.37
------------------------------------------------------------------------------
. eststo One: reg rprice y81 nearinc y81nrinc

Source |       SS           df       MS      Number of obs   =       321
-------------+----------------------------------   F(3, 317)       =     22.25
Model |  6.1055e+10         3  2.0352e+10   Prob > F        =    0.0000
Residual |  2.8994e+11       317   914632739   R-squared       =    0.1739
-------------+----------------------------------   Adj R-squared   =    0.1661
Total |  3.5099e+11       320  1.0969e+09   Root MSE        =     30243

------------------------------------------------------------------------------
rprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
y81 |   18790.29   4050.065     4.64   0.000     10821.88    26758.69
nearinc |  -18824.37   4875.322    -3.86   0.000    -28416.45   -9232.293
y81nrinc |   -11863.9   7456.646    -1.59   0.113    -26534.67    2806.867
_cons |   82517.23    2726.91    30.26   0.000      77152.1    87882.36
------------------------------------------------------------------------------

. eststo Two: qui reg rprice y81 nearinc y81nrinc age agesq
. eststo Three: qui reg rprice y81 nearinc y81nrinc age agesq intst land area rooms b
> aths
. estout, cells(b(nostar fmt(2)) se(par fmt(2))) stats(r2 r2_a N, fmt(%9.3f %9.3f %9.0g)///
labels(R-squared Adj-R-squared Observations)) varlabels(_cons constant) varwidth(20)///
ti("Table 13.2 Effects of Incinerator Location on Housing Prices (rprice)")

Table 13.2 Effects of Incinerator Location on Housing Prices (rprice)
-----------------------------------------------------------
One          Two        Three
b/se         b/se         b/se
-----------------------------------------------------------
y81                      18790.29     21321.04     13928.48
(4050.06)    (3443.63)    (2798.75)
nearinc                 -18824.37      9397.94      3780.34
(4875.32)    (4812.22)    (4453.42)
y81nrinc                -11863.90    -21920.27    -14177.93
(7456.65)    (6359.75)    (4987.27)
age                                   -1494.42      -739.45
(131.86)     (131.13)
agesq                                     8.69         3.45
(0.85)       (0.81)
intst                                                 -0.54
(0.20)
land                                                   0.14
(0.03)
area                                                  18.09
(2.31)
rooms                                               3304.23
(1661.25)
baths                                               6977.32
(2581.32)
constant                 82517.23     89116.54     13807.67
(2726.91)    (2406.05)   (11166.59)
-----------------------------------------------------------
R-squared                   0.174        0.414        0.660
Adj-R-squared               0.166        0.405        0.649
Observations                  321          321          321
-----------------------------------------------------------
. est clear

. reg lprice y81 nearinc y81nrinc

Source |       SS           df       MS      Number of obs   =       321
-------------+----------------------------------   F(3, 317)       =     73.15
Model |  25.1332147         3  8.37773824   Prob > F        =    0.0000
Residual |  36.3057706       317  .114529245   R-squared       =    0.4091
-------------+----------------------------------   Adj R-squared   =    0.4035
Total |  61.4389853       320  .191996829   Root MSE        =    .33842

------------------------------------------------------------------------------
lprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
y81 |   .4569953   .0453207    10.08   0.000     .3678279    .5461627
nearinc |   -.339923   .0545555    -6.23   0.000    -.4472595   -.2325865
y81nrinc |   -.062649   .0834408    -0.75   0.453    -.2268167    .1015187
_cons |   11.28542   .0305145   369.84   0.000     11.22539    11.34546
------------------------------------------------------------------------------

. reg lprice y81 nearinc y81nrinc age agesq lintst lland larea rooms baths

Source |       SS           df       MS      Number of obs   =       321
-------------+----------------------------------   F(10, 310)      =    116.91
Model |  48.5621258        10  4.85621258   Prob > F        =    0.0000
Residual |  12.8768595       310  .041538256   R-squared       =    0.7904
-------------+----------------------------------   Adj R-squared   =    0.7837
Total |  61.4389853       320  .191996829   Root MSE        =    .20381

------------------------------------------------------------------------------
lprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
y81 |    .425974   .0284999    14.95   0.000     .3698963    .4820518
nearinc |    .032232   .0474876     0.68   0.498    -.0612067    .1256708
y81nrinc |  -.1315133   .0519712    -2.53   0.012    -.2337743   -.0292524
age |  -.0083591   .0014111    -5.92   0.000    -.0111358   -.0055825
agesq |   .0000376   8.67e-06     4.34   0.000     .0000206    .0000547
lintst |  -.0614482   .0315075    -1.95   0.052    -.1234438    .0005474
lland |    .099845    .024491     4.08   0.000     .0516554    .1480346
larea |   .3507722   .0514865     6.81   0.000     .2494649    .4520794
rooms |   .0473344   .0173274     2.73   0.007     .0132402    .0814285
baths |   .0942767   .0277256     3.40   0.001     .0397225    .1488309
_cons |   7.651756   .4158832    18.40   0.000     6.833445    8.470067
------------------------------------------------------------------------------

. *Example 13.4. Effect of Worker Compensation Laws on Weeks out of Work
. u injury, clear
. reg ldurat afchnge highearn afhigh if ky==1

Source |       SS           df       MS      Number of obs   =     5,626
-------------+----------------------------------   F(3, 5622)      =     39.54
Model |  191.071442         3  63.6904807   Prob > F        =    0.0000
Residual |   9055.9345     5,622  1.61080301   R-squared       =    0.0207
-------------+----------------------------------   Adj R-squared   =    0.0201
Total |  9247.00594     5,625  1.64391217   Root MSE        =    1.2692

------------------------------------------------------------------------------
ldurat |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
afchnge |   .0076573   .0447173     0.17   0.864    -.0800058    .0953204
highearn |   .2564785   .0474464     5.41   0.000     .1634652    .3494918
afhigh |   .1906012   .0685089     2.78   0.005     .0562973    .3249051
_cons |   1.125615   .0307368    36.62   0.000     1.065359    1.185871
------------------------------------------------------------------------------

. *Example 13.5. Sleeping versus Working
. u slp75_81, clear
. reg cslpnap ctotwrk ceduc cmarr cyngkid cgdhlth

Source |       SS           df       MS      Number of obs   =       239
-------------+----------------------------------   F(5, 233)       =      8.19
Model |  14674698.2         5  2934939.64   Prob > F        =    0.0000
Residual |  83482611.7       233  358294.471   R-squared       =    0.1495
-------------+----------------------------------   Adj R-squared   =    0.1313
Total |  98157309.9       238  412425.672   Root MSE        =    598.58

------------------------------------------------------------------------------
cslpnap |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ctotwrk |  -.2266694    .036054    -6.29   0.000    -.2977029   -.1556359
ceduc |  -.0244717   48.75938    -0.00   1.000    -96.09008    96.04113
cmarr |   104.2139   92.85536     1.12   0.263    -78.72946    287.1574
cyngkid |    94.6654   87.65252     1.08   0.281    -78.02739    267.3582
cgdhlth |   87.57785   76.59913     1.14   0.254    -63.33758    238.4933
_cons |  -92.63404    45.8659    -2.02   0.045    -182.9989   -2.269152
------------------------------------------------------------------------------

. *Example 13.6. Distributed Lag of Crime Rate on Clear-Up Rate
. u crime3, clear
. reg clcrime cclrprc1 cclrprc2

Source |       SS           df       MS      Number of obs   =        53
-------------+----------------------------------   F(2, 50)        =      5.99
Model |  1.42294697         2  .711473484   Prob > F        =    0.0046
Residual |  5.93723904        50  .118744781   R-squared       =    0.1933
-------------+----------------------------------   Adj R-squared   =    0.1611
Total |  7.36018601        52  .141542039   Root MSE        =    .34459

------------------------------------------------------------------------------
clcrime |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
cclrprc1 |  -.0040475   .0047199    -0.86   0.395    -.0135276    .0054326
cclrprc2 |  -.0131966   .0051946    -2.54   0.014    -.0236302   -.0027629
_cons |   .0856556   .0637825     1.34   0.185    -.0424553    .2137665
------------------------------------------------------------------------------

. *Example 13.7. Effect of Drunk Driving Laws on Traffic Fatalities
. u traffic1, clear
. reg cdthrte copen cadmn

Source |       SS           df       MS      Number of obs   =        51
-------------+----------------------------------   F(2, 48)        =      3.23
Model |  .762579785         2  .381289893   Prob > F        =    0.0482
Residual |  5.66369475        48  .117993641   R-squared       =    0.1187
-------------+----------------------------------   Adj R-squared   =    0.0819
Total |  6.42627453        50  .128525491   Root MSE        =     .3435

------------------------------------------------------------------------------
cdthrte |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
copen |  -.4196787   .2055948    -2.04   0.047    -.8330547   -.0063028
cadmn |  -.1506024   .1168223    -1.29   0.204    -.3854894    .0842846
_cons |  -.4967872   .0524256    -9.48   0.000    -.6021959   -.3913784
------------------------------------------------------------------------------

. *Example 13.8. Effect of Enterprise Zones on Unemployment Claims
. u ezunem, clear
. reg guclms d82-d88 cez

Source |       SS           df       MS      Number of obs   =       176
-------------+----------------------------------   F(8, 167)       =     34.50
Model |  12.8826331         8  1.61032914   Prob > F        =    0.0000
Residual |  7.79583815       167  .046681666   R-squared       =    0.6230
-------------+----------------------------------   Adj R-squared   =    0.6049
Total |  20.6784713       175  .118162693   Root MSE        =    .21606

------------------------------------------------------------------------------
guclms |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
d82 |   .7787595   .0651444    11.95   0.000     .6501469    .9073721
d83 |  -.0331192   .0651444    -0.51   0.612    -.1617318    .0954934
d84 |  -.0171382   .0685455    -0.25   0.803    -.1524655    .1181891
d85 |    .323081   .0666774     4.85   0.000     .1914417    .4547202
d86 |    .292154   .0651444     4.48   0.000     .1635413    .4207666
d87 |   .0539481   .0651444     0.83   0.409    -.0746645    .1825607
d88 |  -.0170526   .0651444    -0.26   0.794    -.1456652    .1115601
cez |  -.1818775   .0781862    -2.33   0.021    -.3362382   -.0275169
_cons |  -.3216319    .046064    -6.98   0.000    -.4125748   -.2306891
------------------------------------------------------------------------------

. display exp(_b[cez])-1
-.16629657
. predict u, res
(22 missing values generated)
. g u2=u^2
(22 missing values generated)
. g u_1=u[_n-1]
(23 missing values generated)
. reg u2 d82-d88 cez

Source |       SS           df       MS      Number of obs   =       176
-------------+----------------------------------   F(8, 167)       =      0.85
Model |  .025836793         8  .003229599   Prob > F        =    0.5570
Residual |  .631857421       167  .003783577   R-squared       =    0.0393
-------------+----------------------------------   Adj R-squared   =   -0.0067
Total |  .657694213       175  .003758253   Root MSE        =    .06151

------------------------------------------------------------------------------
u2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
d82 |   -.015372   .0185462    -0.83   0.408    -.0519872    .0212432
d83 |  -.0106696   .0185462    -0.58   0.566    -.0472848    .0259456
d84 |   .0149802   .0195145     0.77   0.444    -.0235467     .053507
d85 |   .0085615   .0189826     0.45   0.653    -.0289154    .0460384
d86 |   .0103385   .0185462     0.56   0.578    -.0262767    .0469538
d87 |   .0112266   .0185462     0.61   0.546    -.0253886    .0478418
d88 |  -.0187891   .0185462    -1.01   0.312    -.0554043    .0178261
cez |  -.0073174   .0222591    -0.33   0.743     -.051263    .0366281
_cons |   .0446758   .0131141     3.41   0.001     .0187849    .0705667
------------------------------------------------------------------------------

. reg u d83-d88 cez u_1

Source |       SS           df       MS      Number of obs   =       154
-------------+----------------------------------   F(8, 145)       =      0.74
Model |  .267609183         8  .033451148   Prob > F        =    0.6551
Residual |  6.54536157       145  .045140425   R-squared       =    0.0393
-------------+----------------------------------   Adj R-squared   =   -0.0137
Total |  6.81297075       153  .044529221   Root MSE        =    .21246

------------------------------------------------------------------------------
u |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
d83 |  -1.33e-09   .0640599    -0.00   1.000    -.1266119    .1266119
d84 |  -.0105917   .0675446    -0.16   0.876     -.144091    .1229075
d85 |  -.0070611   .0656315    -0.11   0.914    -.1367792    .1226569
d86 |  -3.38e-10   .0640599    -0.00   1.000    -.1266119    .1266119
d87 |  -4.28e-09   .0640599    -0.00   1.000    -.1266119    .1266119
d88 |  -4.11e-09   .0640599    -0.00   1.000    -.1266119    .1266119
cez |   .0388363   .0785217     0.49   0.622    -.1163587    .1940313
u_1 |  -.1965359   .0807187    -2.43   0.016    -.3560731   -.0369986
_cons |   1.65e-09   .0452972     0.00   1.000    -.0895281    .0895281
------------------------------------------------------------------------------

. *Example 13.9. County Crime Rates in North Carolina
. u crime4, clear
. eststo hetrosk: qui reg clcrmrte i.year clprbarr clprbcon clprbpri clavgsen clpolpc
. predict u, res
(90 missing values generated)
. eststo robust: qui reg clcrmrte i.year clprbarr clprbcon clprbpri clavgsen clpolpc, r
. estout, cells(b(nostar fmt(2)) se(par fmt(2))) stats(r2 r2_a N, fmt(%9.3f %9.3f %9.0g)///
labels(R-squared Adj-R-squared Observations)) varlabels(_cons constant) varwidth(20)///
ti("Dependent Variable is clcrmrte")

Dependent Variable is clcrmrte
----------------------------------------------
hetrosk       robust
b/se         b/se
----------------------------------------------
82.year                    0.0000       0.0000
(.)          (.)
83.year                   -0.0999      -0.0999
(0.0239)     (0.0216)
84.year                   -0.0479      -0.0479
(0.0235)     (0.0203)
85.year                   -0.0046      -0.0046
(0.0235)     (0.0241)
86.year                    0.0275       0.0275
(0.0241)     (0.0215)
87.year                    0.0408       0.0408
(0.0244)     (0.0235)
clprbarr                  -0.3275      -0.3275
(0.0300)     (0.0515)
clprbcon                  -0.2381      -0.2381
(0.0182)     (0.0312)
clprbpri                  -0.1650      -0.1650
(0.0260)     (0.0351)
clavgsen                  -0.0218      -0.0218
(0.0221)     (0.0250)
clpolpc                    0.3984       0.3984
(0.0269)     (0.0759)
constant                   0.0077       0.0077
(0.0171)     (0.0146)
----------------------------------------------
R-squared                   0.433        0.433
Observations                  540          540
----------------------------------------------
. est clear

. g usq=u^2
(90 missing values generated)
. g u_1=u[_n-1]
(91 missing values generated)
. reg usq i.year clprbarr clprbcon clprbpri clavgsen clpolpc

Source |       SS           df       MS      Number of obs   =       540
-------------+----------------------------------   F(10, 529)      =      1.09
Model |  .037538086        10  .003753809   Prob > F        =    0.3655
Residual |   1.8170922       529  .003434957   R-squared       =    0.0202
-------------+----------------------------------   Adj R-squared   =    0.0017
Total |  1.85463029       539  .003440873   Root MSE        =    .05861

------------------------------------------------------------------------------
usq |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
year |
83  |   .0047299   .0090756     0.52   0.602    -.0130988    .0225586
84  |  -.0015107   .0089263    -0.17   0.866    -.0190461    .0160247
85  |   .0172255   .0089255     1.93   0.054    -.0003082    .0347591
86  |   .0025074   .0091722     0.27   0.785    -.0155109    .0205258
87  |   .0086536   .0092732     0.93   0.351    -.0095631    .0268703
|
clprbarr |  -.0145322   .0113867    -1.28   0.202     -.036901    .0078365
clprbcon |   .0018215   .0069255     0.26   0.793    -.0117833    .0154264
clprbpri |   .0052474   .0098633     0.53   0.595    -.0141286    .0246234
clavgsen |   .0034765   .0083903     0.41   0.679     -.013006     .019959
clpolpc |   .0016434   .0102101     0.16   0.872    -.0184138    .0217006
_cons |   .0181115   .0064787     2.80   0.005     .0053843    .0308388
------------------------------------------------------------------------------

. reg u i.year clprbarr clprbcon clprbpri clavgsen clpolpc u_1

Source |       SS           df       MS      Number of obs   =       450
-------------+----------------------------------   F(10, 439)      =      2.35
Model |  .564663977        10  .056466398   Prob > F        =    0.0102
Residual |  10.5288381       439  .023983686   R-squared       =    0.0509
-------------+----------------------------------   Adj R-squared   =    0.0293
Total |  11.0935021       449  .024707132   Root MSE        =    .15487

------------------------------------------------------------------------------
u |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
year |
84  |  -.0004163   .0234297    -0.02   0.986    -.0464646    .0456319
85  |   .0006335   .0232143     0.03   0.978    -.0449915    .0462585
86  |    .002122   .0234036     0.09   0.928    -.0438751    .0481191
87  |   .0012379   .0234019     0.05   0.958    -.0447558    .0472317
|
clprbarr |   .0082875   .0330698     0.25   0.802    -.0567074    .0732823
clprbcon |  -.0036161   .0200155    -0.18   0.857    -.0429542    .0357221
clprbpri |   .0017131    .027855     0.06   0.951    -.0530326    .0564588
clavgsen |  -.0125831   .0245006    -0.51   0.608    -.0607362      .03557
clpolpc |   .0214883   .0282688     0.76   0.448    -.0340706    .0770473
u_1 |  -.2332117   .0488802    -4.77   0.000      -.32928   -.1371435
_cons |  -.0007033   .0165046    -0.04   0.966    -.0331411    .0317346
------------------------------------------------------------------------------

. log close
name:  SN
log:  ~Wooldridge\intro-econx\iexample13.smcl
log type:  smcl
closed on:  16 Jan 2019, 13:12:22
-------------------------------------------------------------------------------------
```