quan tile
DESCRIPTION
ghrgtjTRANSCRIPT
QUANTILE REGRESSION
. qreg wages educ
Iteration 1: WLS sum of weighted deviations = 9.199e+09
Iteration 1: sum of abs. weighted deviations = 9.201e+09
Iteration 2: sum of abs. weighted deviations = 9.123e+09
note: alternate solutions exist
Iteration 3: sum of abs. weighted deviations = 8.543e+09
note: alternate solutions exist
Iteration 4: sum of abs. weighted deviations = 8.537e+09
note: alternate solutions exist
Iteration 5: sum of abs. weighted deviations = 8.553e+09
note: alternate solutions exist
Iteration 6: sum of abs. weighted deviations = 8.553e+09
Iteration 7: sum of abs. weighted deviations = 8.553e+09
Median regression Number of obs = 151031
Raw sum of deviations 8.87e+09 (about 32650)
Min sum of deviations 8.55e+09 Pseudo R2 = 0.0361
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wages | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | 1.47799 .2099875 7.04 0.000 1.066419 1.889561
_cons | 43174.99 3702.582 11.66 0.000 35918.01 50431.98
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QUANTILE REGRESSION FOR A SPECIFIC QUANTILE
. qreg wages educ,quantile(.25)
Iteration 1: WLS sum of weighted deviations = 8.284e+09
Iteration 1: sum of abs. weighted deviations = 8.284e+09
Iteration 2: sum of abs. weighted deviations = 7.570e+09
note: alternate solutions exist
Iteration 3: sum of abs. weighted deviations = 5.056e+09
note: alternate solutions exist
Iteration 4: sum of abs. weighted deviations = 5.055e+09
note: alternate solutions exist
Iteration 5: sum of abs. weighted deviations = 5.055e+09
note: alternate solutions exist
Iteration 6: sum of abs. weighted deviations = 5.055e+09
Iteration 7: sum of abs. weighted deviations = 5.055e+09
.25 Quantile regression Number of obs = 151031
Raw sum of deviations 5.10e+09 (about 5000)
Min sum of deviations 5.05e+09 Pseudo R2 = 0.0089
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wages | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.0328504 .0134833 -2.44 0.015 -.0592775 -.0064234
_cons | 4032.062 283.5979 14.22 0.000 3476.216 4587.908
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.
Simultaneous Quantile
sqreg wages educ,quantile(.25 0.5 0.75)
(fitting base model)
(bootstrapping convergence not achieved
*convergence not achieved
*convergence not achieved
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*convergence not achieved
*..convergence not achieved
*..convergence not achieved
*convergence not achieved
*convergence not achieved
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*.convergence not achieved
*.convergence not achieved
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*.convergence not achieved
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*.convergence not achieved
*convergence not achieved
*convergence not achieved
Simultaneous quantile regression Number of obs = 151031
bootstrap(20) SEs .25 Pseudo R2 = 0.0089
.50 Pseudo R2 = 0.0361
.75 Pseudo R2 = 0.0890
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| Bootstrap
wages | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q25 |
educ | -.0328504 .8678359 -0.04 0.970 -1.733791 1.66809
_cons | 4032.062 13682.43 0.29 0.768 -22785.22 30849.35
-------------+----------------------------------------------------------------
q50 |
educ | 1.47799 .5042082 2.93 0.003 .4897523 2.466228
_cons | 43174.99 13740.12 3.14 0.002 16244.64 70105.35
-------------+----------------------------------------------------------------
q75 |
educ | 4.415372 2.198365 2.01 0.045 .1066215 8.724122
_cons | 71971.3 40824.58 1.76 0.078 -8044.056 151986.7
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