CHAPTER 13 - Pooling Cross Sections across Time: Simple Panel Data Methods#
import stata_setup
stata_setup.config("C:/Program Files/Stata18/", "se" ,splash=False)
Example 13.1 Women’s Fertility over Time#
%%stata
u fertil1, clear
reg kids educ age agesq black-y84
test y74 y76 y78 y80 y82 y84
predict u, res
g u2 = u^2
qui reg u2 educ age agesq black-y84
display e(N) * e(r2)
. 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 | Coefficient 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#
%%stata
u cps78_85, clear
reg lwage y85 educ y85educ exper expersq union female y85fem
display "Return to Education in 1978 is " _b[educ]*100 "%"
display "Return to Education in 1985 is " (_b[educ] + _b[y85educ])*100 "%"
. 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 | Coefficient 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#
%%stata
u KIELMC, clear
reg rprice nearinc if year==1981
reg rprice nearinc if year==1978
eststo One: reg rprice y81 nearinc y81nrinc
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)")
est clear
reg lprice y81 nearinc y81nrinc
reg lprice y81 nearinc y81nrinc age agesq lintst lland larea rooms baths
. 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 | Coefficient 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 | Coefficient 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 | Coefficient 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 r
> ooms 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 const
> ant) varwidth(20) ti("Table 13.2 Effects of Incinerator Location on Housing P
> rices (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 | Coefficient 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 | Coefficient 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#
%%stata
u injury, clear
reg ldurat afchnge highearn afhigh if ky==1
. 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 | Coefficient 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#
%%stata
u slp75_81, clear
reg cslpnap ctotwrk ceduc cmarr cyngkid cgdhlth
. 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 | Coefficient 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#
%%stata
u crime3, clear
reg clcrime cclrprc1 cclrprc2
. 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 | Coefficient 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#
%%stata
u traffic1, clear
reg cdthrte copen cadmn
. 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 | Coefficient 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#
%%stata
u ezunem, clear
reg guclms d82-d88 cez
display exp(_b[cez])-1
predict u, res
g u2=u^2
g u_1=u[_n-1]
reg u2 d82-d88 cez
reg u d83-d88 cez u_1
. 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 | Coefficient 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 | Coefficient 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 | Coefficient 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#
%%stata
u crime4, clear
eststo hetrosk: qui reg clcrmrte i.year clprbarr clprbcon clprbpri clavgsen clpolpc
predict u, res
eststo robust: qui reg clcrmrte i.year clprbarr clprbcon clprbpri clavgsen clpolpc, r
estout, cells(b(nostar fmt(4)) se(par fmt(4))) 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")
est clear
. u crime4, clear
. eststo hetrosk: qui reg clcrmrte i.year clprbarr clprbcon clprbpri clavgsen c
> lpolpc
. predict u, res
(90 missing values generated)
. eststo robust: qui reg clcrmrte i.year clprbarr clprbcon clprbpri clavgsen cl
> polpc, r
. estout, cells(b(nostar fmt(4)) se(par fmt(4))) stats(r2 r2_a N, fmt(%9.3f %9.
> 3f %9.0g) labels(R-squared Adj-R-squared Observations)) varlabels(_cons const
> ant) 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
.
%%stata
g usq=u^2
g u_1=u[_n-1]
reg usq i.year clprbarr clprbcon clprbpri clavgsen clpolpc
reg u i.year clprbarr clprbcon clprbpri clavgsen clpolpc u_1
. 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 | Coefficient 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 | Coefficient 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
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.