INTRODUCTORY ECONOMETRICS – REPLICATING EXAMPLES
Chapter 13 Simple Panel Data Methods – 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
. reg u2 educ age agesq black-y84
Source | SS df MS Number of obs = 1,129
-------------+---------------------------------- F(17, 1111) = 3.37
Model | 601.163969 17 35.3625864 Prob > F = 0.0000
Residual | 11668.7363 1,111 10.502913 R-squared = 0.0490
-------------+---------------------------------- Adj R-squared = 0.0344
Total | 12269.9003 1,128 10.8775712 Root MSE = 3.2408
------------------------------------------------------------------------------
u2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.1024471 .0382446 -2.68 0.007 -.1774869 -.0274073
age | .2938519 .2884432 1.02 0.309 -.272103 .8598067
agesq | -.0026578 .0032605 -0.82 0.415 -.0090552 .0037396
black | 1.341004 .361706 3.71 0.000 .6313003 2.050708
east | -.0652052 .2767741 -0.24 0.814 -.608264 .4778536
northcen | .1681138 .2519895 0.67 0.505 -.3263151 .6625427
west | .1241666 .347903 0.36 0.721 -.5584545 .8067876
farm | -.4401572 .306793 -1.43 0.152 -1.042116 .1618019
othrural | -.0704047 .3656796 -0.19 0.847 -.7879052 .6470958
town | .0373982 .2595641 0.14 0.885 -.471893 .5466894
smcity | -.3669136 .3341102 -1.10 0.272 -1.022472 .2886446
y74 | -.9770124 .3599978 -2.71 0.007 -1.683365 -.2706603
y76 | -.6071817 .3731906 -1.63 0.104 -1.339419 .1250561
y78 | -.7446627 .3786894 -1.97 0.049 -1.48769 -.0016358
y80 | -1.05273 .380955 -2.76 0.006 -1.800203 -.3052577
y82 | -.8563934 .3594143 -2.38 0.017 -1.561601 -.151186
y84 | -1.031562 .3637499 -2.84 0.005 -1.745276 -.317848
_cons | -3.257907 6.3609 -0.51 0.609 -15.73864 9.222824
------------------------------------------------------------------------------
. estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of u2
chi2(1) = 79.03
Prob > chi2 = 0.0000
. *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 baths
. 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
Adj-R-squared 0.422 0.422
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
-------------------------------------------------------------------------------------
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