Example 12.1: Testing for AR(1) Serial Correlation in the Phillips Curve
use http://fmwww.bc.edu/ec-p/data/wooldridge/phillips
tsset year, yearly
reg inf unem
Source | SS df MS Number of obs = 49
-------------+------------------------------ F( 1, 47) = 2.62
Model | 25.6369575 1 25.6369575 Prob > F = 0.1125
Residual | 460.61979 47 9.80042107 R-squared = 0.0527
-------------+------------------------------ Adj R-squared = 0.0326
Total | 486.256748 48 10.1303489 Root MSE = 3.1306
------------------------------------------------------------------------------
inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem | .4676257 .2891262 1.62 0.112 -.1140212 1.049273
_cons | 1.42361 1.719015 0.83 0.412 -2.034602 4.881822
------------------------------------------------------------------------------
predict double uh, resid
reg uh L.uh
Source | SS df MS Number of obs = 48
-------------+------------------------------ F( 1, 46) = 24.34
Model | 150.91704 1 150.91704 Prob > F = 0.0000
Residual | 285.198417 46 6.19996558 R-squared = 0.3460
-------------+------------------------------ Adj R-squared = 0.3318
Total | 436.115457 47 9.27905227 Root MSE = 2.49
------------------------------------------------------------------------------
uh | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uh |
L1 | .5729695 .1161334 4.93 0.000 .3392052 .8067338
_cons | -.1133967 .359404 -0.32 0.754 -.8368393 .610046
------------------------------------------------------------------------------
reg cinf unem
Source | SS df MS Number of obs = 48
-------------+------------------------------ F( 1, 46) = 5.56
Model | 33.3829988 1 33.3829988 Prob > F = 0.0227
Residual | 276.30513 46 6.00663326 R-squared = 0.1078
-------------+------------------------------ Adj R-squared = 0.0884
Total | 309.688129 47 6.58910913 Root MSE = 2.4508
------------------------------------------------------------------------------
cinf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem | -.5425869 .2301559 -2.36 0.023 -1.005867 -.079307
_cons | 3.030581 1.37681 2.20 0.033 .2592061 5.801955
------------------------------------------------------------------------------
predict double uh2, resid
reg uh2 L.uh2
Source | SS df MS Number of obs = 47
-------------+------------------------------ F( 1, 45) = 0.08
Model | .350023883 1 .350023883 Prob > F = 0.7752
Residual | 190.837374 45 4.24083054 R-squared = 0.0018
-------------+------------------------------ Adj R-squared = -0.0204
Total | 191.187398 46 4.15624779 Root MSE = 2.0593
------------------------------------------------------------------------------
uh2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uh2 |
L1 | -.0355928 .1238908 -0.29 0.775 -.2851216 .213936
_cons | .1941655 .3003839 0.65 0.521 -.4108387 .7991698
------------------------------------------------------------------------------
Example 12.2: Testing for AR(1) Serial Correlation in the Minimum Wage Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/prminwge
tsset year, yearly
reg lprepop lmincov lprgnp lusgnp t
Source | SS df MS Number of obs = 38
-------------+------------------------------ F( 4, 33) = 66.23
Model | .284429802 4 .071107451 Prob > F = 0.0000
Residual | .035428549 33 .001073592 R-squared = 0.8892
-------------+------------------------------ Adj R-squared = 0.8758
Total | .319858351 37 .00864482 Root MSE = .03277
------------------------------------------------------------------------------
lprepop | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lmincov | -.2122611 .0401525 -5.29 0.000 -.293952 -.1305703
lprgnp | .2852399 .0804923 3.54 0.001 .1214771 .4490027
lusgnp | .4860416 .2219838 2.19 0.036 .0344121 .937671
t | -.0266632 .0046267 -5.76 0.000 -.0360764 -.01725
_cons | -6.663407 1.257838 -5.30 0.000 -9.222497 -4.104317
------------------------------------------------------------------------------
predict uh, res
reg uh lmincov lprgnp lusgnp t L.uh
Source | SS df MS Number of obs = 37
-------------+------------------------------ F( 5, 31) = 1.98
Model | .007527219 5 .001505444 Prob > F = 0.1089
Residual | .023530495 31 .000759048 R-squared = 0.2424
-------------+------------------------------ Adj R-squared = 0.1202
Total | .031057714 36 .000862714 Root MSE = .02755
------------------------------------------------------------------------------
uh | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lmincov | .0375001 .0352124 1.06 0.295 -.0343161 .1093164
lprgnp | -.0784652 .0705241 -1.11 0.274 -.2223 .0653696
lusgnp | .2039314 .1951597 1.04 0.304 -.1940995 .6019622
t | -.0034662 .0040736 -0.85 0.401 -.0117744 .0048419
uh |
L1 | .4805079 .1664442 2.89 0.007 .1410428 .819973
_cons | -.8507673 1.092697 -0.78 0.442 -3.079338 1.377804
------------------------------------------------------------------------------
reg uh L.uh
Source | SS df MS Number of obs = 37
-------------+------------------------------ F( 1, 35) = 6.89
Model | .005111108 1 .005111108 Prob > F = 0.0127
Residual | .025946606 35 .000741332 R-squared = 0.1646
-------------+------------------------------ Adj R-squared = 0.1407
Total | .031057714 36 .000862714 Root MSE = .02723
------------------------------------------------------------------------------
uh | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uh |
L1 | .4173216 .158935 2.63 0.013 .0946664 .7399768
_cons | -.0008953 .0044883 -0.20 0.843 -.0100071 .0082166
------------------------------------------------------------------------------
Example 12.3: Testing for AR(3) Serial Correlation
use http://fmwww.bc.edu/ec-p/data/wooldridge/barium
tsset t, yearly
reg lchnimp lchempi lgas lrtwex befile6 affile6 afdec6
Source | SS df MS Number of obs = 131
-------------+------------------------------ F( 6, 124) = 9.06
Model | 19.4051456 6 3.23419093 Prob > F = 0.0000
Residual | 44.2471061 124 .356831501 R-squared = 0.3049
-------------+------------------------------ Adj R-squared = 0.2712
Total | 63.6522517 130 .489632706 Root MSE = .59735
------------------------------------------------------------------------------
lchnimp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lchempi | 3.1172 .479202 6.50 0.000 2.168725 4.065675
lgas | .1963049 .9066233 0.22 0.829 -1.598157 1.990766
lrtwex | .9830093 .4001536 2.46 0.015 .1909934 1.775025
befile6 | .0595742 .26097 0.23 0.820 -.4569584 .5761068
affile6 | -.0324067 .2642973 -0.12 0.903 -.5555252 .4907118
afdec6 | -.5652446 .2858353 -1.98 0.050 -1.130993 .0005035
_cons | -17.80195 21.04551 -0.85 0.399 -59.45692 23.85301
------------------------------------------------------------------------------
predict uh, res
reg uh lchempi lgas lrtwex befile6 affile6 afdec6 L(1/3).uh
Source | SS df MS Number of obs = 128
-------------+------------------------------ F( 9, 118) = 1.72
Model | 5.03366421 9 .559296023 Prob > F = 0.0920
Residual | 38.3937238 118 .325370541 R-squared = 0.1159
-------------+------------------------------ Adj R-squared = 0.0485
Total | 43.427388 127 .341947937 Root MSE = .57041
------------------------------------------------------------------------------
uh | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lchempi | -.1431572 .4720255 -0.30 0.762 -1.077896 .7915818
lgas | .6232994 .8859803 0.70 0.483 -1.131183 2.377782
lrtwex | .1786641 .3910344 0.46 0.649 -.5956904 .9530186
befile6 | -.0859232 .2510069 -0.34 0.733 -.5829851 .4111387
affile6 | -.1221207 .2546985 -0.48 0.632 -.6264931 .3822517
afdec6 | -.0668277 .2743671 -0.24 0.808 -.6101492 .4764937
uh |
L1 | .2214896 .0916573 2.42 0.017 .0399832 .4029959
L2 | .1340417 .0921595 1.45 0.148 -.0484592 .3165427
L3 | .125542 .0911194 1.38 0.171 -.0548992 .3059831
_cons | -14.36897 20.65581 -0.70 0.488 -55.27309 26.53516
------------------------------------------------------------------------------
test L1.uh L2.uh L3.uh
( 1) L.uh = 0.0
( 2) L2.uh = 0.0
( 3) L3.uh = 0.0
F( 3, 118) = 5.12
Prob > F = 0.0023
Example 12.4: Cohrane-Orcutt Estimation in the Event Study
use http://fmwww.bc.edu/ec-p/data/wooldridge/barium
reg lchnimp lchempi lgas lrtwex befile6 affile6 afdec6
Source | SS df MS Number of obs = 131
-------------+------------------------------ F( 6, 124) = 9.06
Model | 19.4051456 6 3.23419093 Prob > F = 0.0000
Residual | 44.2471061 124 .356831501 R-squared = 0.3049
-------------+------------------------------ Adj R-squared = 0.2712
Total | 63.6522517 130 .489632706 Root MSE = .59735
------------------------------------------------------------------------------
lchnimp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lchempi | 3.1172 .479202 6.50 0.000 2.168725 4.065675
lgas | .1963049 .9066233 0.22 0.829 -1.598157 1.990766
lrtwex | .9830093 .4001536 2.46 0.015 .1909934 1.775025
befile6 | .0595742 .26097 0.23 0.820 -.4569584 .5761068
affile6 | -.0324067 .2642973 -0.12 0.903 -.5555252 .4907118
afdec6 | -.5652446 .2858353 -1.98 0.050 -1.130993 .0005035
_cons | -17.80195 21.04551 -0.85 0.399 -59.45692 23.85301
------------------------------------------------------------------------------
tsset t
prais lchnimp lchempi lgas lrtwex befile6 affile6 afdec6, corc
Iteration 0: rho = 0.0000
Iteration 1: rho = 0.2708
Iteration 2: rho = 0.2912
Iteration 3: rho = 0.2931
Iteration 4: rho = 0.2933
Iteration 5: rho = 0.2934
Iteration 6: rho = 0.2934
Iteration 7: rho = 0.2934
Cochrane-Orcutt AR(1) regression -- iterated estimates
Source | SS df MS Number of obs = 130
-------------+------------------------------ F( 6, 123) = 4.88
Model | 9.7087769 6 1.61812948 Prob > F = 0.0002
Residual | 40.7583376 123 .331368598 R-squared = 0.1924
-------------+------------------------------ Adj R-squared = 0.1530
Total | 50.4671145 129 .391217942 Root MSE = .57565
------------------------------------------------------------------------------
lchnimp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lchempi | 2.947445 .6455564 4.57 0.000 1.669605 4.225284
lgas | 1.054786 .9909084 1.06 0.289 -.9066561 3.016229
lrtwex | 1.136903 .5135093 2.21 0.029 .1204431 2.153364
befile6 | -.0163727 .3207215 -0.05 0.959 -.6512212 .6184757
affile6 | -.0330837 .3231511 -0.10 0.919 -.6727414 .6065741
afdec6 | -.5771574 .3434533 -1.68 0.095 -1.257002 .1026874
_cons | -37.32057 23.22152 -1.61 0.111 -83.28615 8.645004
-------------+----------------------------------------------------------------
rho | .2933587
------------------------------------------------------------------------------
Durbin-Watson statistic (original) 1.458417
Durbin-Watson statistic (transformed) 2.063302
Example 12.5: Static Phillips Curve
use http://fmwww.bc.edu/ec-p/data/wooldridge/phillips
reg lchnimp lchempi lgas lrtwex befile6 affile6 afdec6
Source | SS df MS Number of obs = 49
-------------+------------------------------ F( 1, 47) = 2.62
Model | 25.6369575 1 25.6369575 Prob > F = 0.1125
Residual | 460.61979 47 9.80042107 R-squared = 0.0527
-------------+------------------------------ Adj R-squared = 0.0326
Total | 486.256748 48 10.1303489 Root MSE = 3.1306
------------------------------------------------------------------------------
inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem | .4676257 .2891262 1.62 0.112 -.1140212 1.049273
_cons | 1.42361 1.719015 0.83 0.412 -2.034602 4.881822
------------------------------------------------------------------------------
tsset year
prais inf unem, corc
Iteration 0: rho = 0.0000
Iteration 1: rho = 0.5727
Iteration 2: rho = 0.7160
Iteration 3: rho = 0.7611
Iteration 4: rho = 0.7715
Iteration 5: rho = 0.7735
Iteration 6: rho = 0.7740
Iteration 7: rho = 0.7740
Iteration 8: rho = 0.7740
Iteration 9: rho = 0.7741
Iteration 10: rho = 0.7741
Cochrane-Orcutt AR(1) regression -- iterated estimates
Source | SS df MS Number of obs = 48
-------------+------------------------------ F( 1, 46) = 4.33
Model | 22.4790685 1 22.4790685 Prob > F = 0.0430
Residual | 238.604008 46 5.18704365 R-squared = 0.0861
-------------+------------------------------ Adj R-squared = 0.0662
Total | 261.083076 47 5.55495907 Root MSE = 2.2775
------------------------------------------------------------------------------
inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem | -.6653356 .3196035 -2.08 0.043 -1.308664 -.0220071
_cons | 7.583458 2.38053 3.19 0.003 2.7917 12.37522
-------------+----------------------------------------------------------------
rho | .7740512
------------------------------------------------------------------------------
Durbin-Watson statistic (original) 0.802700
Durbin-Watson statistic (transformed) 1.593634
Example 12.6: Differencing the Interest Rate Equation
use http://fmwww.bc.edu/ec-p/data/wooldridge/intdef
tsset year
reg i3 inf def
Source | SS df MS Number of obs = 49
-------------+------------------------------ F( 2, 46) = 52.78
Model | 294.032897 2 147.016449 Prob > F = 0.0000
Residual | 128.133943 46 2.78552049 R-squared = 0.6965
-------------+------------------------------ Adj R-squared = 0.6833
Total | 422.16684 48 8.7951425 Root MSE = 1.669
------------------------------------------------------------------------------
i3 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inf | .6131825 .0757753 8.09 0.000 .4606547 .7657104
def | .7004054 .11807 5.93 0.000 .4627427 .938068
_cons | 1.252032 .4416346 2.83 0.007 .3630674 2.140996
------------------------------------------------------------------------------
dwstat
Durbin-Watson d-statistic( 3, 49) = .9142607
predict uh, res
reg uh L.uh
Source | SS df MS Number of obs = 48
-------------+------------------------------ F( 1, 46) = 18.48
Model | 35.6747689 1 35.6747689 Prob > F = 0.0001
Residual | 88.824587 46 1.93096928 R-squared = 0.2865
-------------+------------------------------ Adj R-squared = 0.2710
Total | 124.499356 47 2.64892247 Root MSE = 1.3896
------------------------------------------------------------------------------
uh | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uh |
L1 | .5295517 .1232013 4.30 0.000 .2815602 .7775431
_cons | .0497676 .2005853 0.25 0.805 -.3539896 .4535247
------------------------------------------------------------------------------
reg ci3 cinf cdef
Source | SS df MS Number of obs = 48
-------------+------------------------------ F( 2, 45) = 4.32
Model | 14.4340809 2 7.21704047 Prob > F = 0.0193
Residual | 75.2395041 45 1.67198898 R-squared = 0.1610
-------------+------------------------------ Adj R-squared = 0.1237
Total | 89.673585 47 1.90794862 Root MSE = 1.2931
------------------------------------------------------------------------------
ci3 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cinf | .1683474 .100197 1.68 0.100 -.0334596 .3701544
cdef | -.1075013 .1719174 -0.63 0.535 -.4537607 .238758
_cons | .1144652 .18737 0.61 0.544 -.2629172 .4918477
------------------------------------------------------------------------------
dwstat
Durbin-Watson d-statistic( 3, 48) = 1.806339
predict uh2, res
reg uh2 L.uh2
Source | SS df MS Number of obs = 47
-------------+------------------------------ F( 1, 45) = 0.22
Model | .342371554 1 .342371554 Prob > F = 0.6432
Residual | 70.8327461 45 1.57406102 R-squared = 0.0048
-------------+------------------------------ Adj R-squared = -0.0173
Total | 71.1751176 46 1.54728517 Root MSE = 1.2546
------------------------------------------------------------------------------
uh2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uh2 |
L1 | .0677054 .1451729 0.47 0.643 -.2246878 .3600986
_cons | -.0435038 .1830186 -0.24 0.813 -.4121222 .3251146
------------------------------------------------------------------------------
Example 12.7: The Puerto Rican Minimum Wage
use http://fmwww.bc.edu/ec-p/data/wooldridge/prminwge
tsset t
reg lprepop lmincov lprgnp lusgnp t
Source | SS df MS Number of obs = 38
-------------+------------------------------ F( 4, 33) = 66.23
Model | .284429802 4 .071107451 Prob > F = 0.0000
Residual | .035428549 33 .001073592 R-squared = 0.8892
-------------+------------------------------ Adj R-squared = 0.8758
Total | .319858351 37 .00864482 Root MSE = .03277
------------------------------------------------------------------------------
lprepop | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lmincov | -.2122611 .0401525 -5.29 0.000 -.293952 -.1305703
lprgnp | .2852399 .0804923 3.54 0.001 .1214771 .4490027
lusgnp | .4860416 .2219838 2.19 0.036 .0344121 .937671
t | -.0266632 .0046267 -5.76 0.000 -.0360764 -.01725
_cons | -6.663407 1.257838 -5.30 0.000 -9.222497 -4.104317
------------------------------------------------------------------------------
newey lprepop lmincov lprgnp lusgnp t, lag(2)
Regression with Newey-West standard errors Number of obs = 38
maximum lag : 2 F( 4, 33) = 37.84
Prob > F = 0.0000
------------------------------------------------------------------------------
| Newey-West
lprepop | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lmincov | -.2122611 .0457188 -4.64 0.000 -.3052768 -.1192455
lprgnp | .2852399 .0996364 2.86 0.007 .082528 .4879518
lusgnp | .4860416 .2791144 1.74 0.091 -.081821 1.053904
t | -.0266632 .0057559 -4.63 0.000 -.0383736 -.0149528
_cons | -6.663407 1.536445 -4.34 0.000 -9.789328 -3.537485
------------------------------------------------------------------------------
prais lprepop lmincov lprgnp lusgnp t, corc
Cochrane-Orcutt AR(1) regression -- iterated estimates
Source | SS df MS Number of obs = 37
-------------+------------------------------ F( 4, 32) = 11.06
Model | .031015685 4 .007753921 Prob > F = 0.0000
Residual | .022428371 32 .000700887 R-squared = 0.5803
-------------+------------------------------ Adj R-squared = 0.5279
Total | .053444056 36 .001484557 Root MSE = .02647
------------------------------------------------------------------------------
lprepop | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lmincov | -.110755 .0446556 -2.48 0.019 -.2017155 -.0197944
lprgnp | .2673698 .1119371 2.39 0.023 .0393614 .4953782
lusgnp | .3664558 .2201901 1.66 0.106 -.0820568 .8149684
t | -.0243278 .005792 -4.20 0.000 -.0361256 -.01253
_cons | -5.51891 1.339621 -4.12 0.000 -8.24763 -2.790191
-------------+----------------------------------------------------------------
rho | .643343
------------------------------------------------------------------------------
Durbin-Watson statistic (original) 1.013709
Durbin-Watson statistic (transformed) 1.630403
Example 12.8: Heteroscedasticity and the Efficient Markets Hypothesis
use http://fmwww.bc.edu/ec-p/data/wooldridge/nyse
reg return return_1
Source | SS df MS Number of obs = 689
-------------+------------------------------ F( 1, 687) = 2.40
Model | 10.6866237 1 10.6866237 Prob > F = 0.1218
Residual | 3059.73813 687 4.4537673 R-squared = 0.0035
-------------+------------------------------ Adj R-squared = 0.0020
Total | 3070.42476 688 4.46282668 Root MSE = 2.1104
------------------------------------------------------------------------------
return | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
return_1 | .0588984 .0380231 1.55 0.122 -.0157569 .1335538
_cons | .179634 .0807419 2.22 0.026 .0211034 .3381646
------------------------------------------------------------------------------
predict uh, res
gen uh2=uh^2
bpagan return_1
Breusch-Pagan LM statistic: 95.21722 Chi-sq( 1) P-value = 1.7e-22
reg uh2 return_1
Source | SS df MS Number of obs = 689
-------------+------------------------------ F( 1, 687) = 30.05
Model | 3755.56757 1 3755.56757 Prob > F = 0.0000
Residual | 85846.3162 687 124.958248 R-squared = 0.0419
-------------+------------------------------ Adj R-squared = 0.0405
Total | 89601.8838 688 130.235296 Root MSE = 11.178
------------------------------------------------------------------------------
uh2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
return_1 | -1.104132 .2014029 -5.48 0.000 -1.499572 -.7086932
_cons | 4.656501 .4276789 10.89 0.000 3.816786 5.496216
------------------------------------------------------------------------------
Example 12.9: ARCH in Stock Returns
use http://fmwww.bc.edu/ec-p/data/wooldridge/nyse
tsset t
reg return return_1
Source | SS df MS Number of obs = 689
-------------+------------------------------ F( 1, 687) = 2.40
Model | 10.6866237 1 10.6866237 Prob > F = 0.1218
Residual | 3059.73813 687 4.4537673 R-squared = 0.0035
-------------+------------------------------ Adj R-squared = 0.0020
Total | 3070.42476 688 4.46282668 Root MSE = 2.1104
------------------------------------------------------------------------------
return | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
return_1 | .0588984 .0380231 1.55 0.122 -.0157569 .1335538
_cons | .179634 .0807419 2.22 0.026 .0211034 .3381646
------------------------------------------------------------------------------
predict uh1, res
gen uh21=uh1^2
gen uh21_1=uh1[_n-1]^2
archlm
ARCH LM test statistic, order( 1): 78.16118 Chi-sq( 1) P-value = 9.5e-19
reg uh21 uh21_1
Source | SS df MS Number of obs = 688
-------------+------------------------------ F( 1, 686) = 87.92
Model | 10177.7088 1 10177.7088 Prob > F = 0.0000
Residual | 79409.7826 686 115.757701 R-squared = 0.1136
-------------+------------------------------ Adj R-squared = 0.1123
Total | 89587.4914 687 130.403918 Root MSE = 10.759
------------------------------------------------------------------------------
uh21 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uh21_1 | .3370622 .0359468 9.38 0.000 .2664833 .4076411
_cons | 2.947434 .4402343 6.70 0.000 2.083065 3.811802
------------------------------------------------------------------------------
reg uh1 L.uh1
Source | SS df MS Number of obs = 688
-------------+------------------------------ F( 1, 686) = 0.00
Model | .006037908 1 .006037908 Prob > F = 0.9707
Residual | 3059.0813 686 4.45930219 R-squared = 0.0000
-------------+------------------------------ Adj R-squared = -0.0015
Total | 3059.08734 687 4.45282 Root MSE = 2.1117
------------------------------------------------------------------------------
uh1 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
uh1 |
L1 | .0014048 .0381773 0.04 0.971 -.0735537 .0763633
_cons | -.0011708 .080508 -0.01 0.988 -.1592425 .156901
------------------------------------------------------------------------------
This page prepared by Oleksandr Talavera (revised 8 Nov 2002)
Send your questions/comments/suggestions to Kit Baum
at baum@bc.edu
These pages are maintained by the Faculty Micro
Resource Center's GSA Program,
a unit of Boston College Academic Technology Services