[Can Jin]

Hi, dear @FernandoRios

my code is
mmqreg y x ,q(0.25) absorb(v1) nols

then send me error code like this
last estimates not found
r(301);

I do not understand why should have last estimates
Is there a problem with the source code?

if not add abs(v1)
just like
mmqreg y x ,q(0.25) nols
it can send me results

but I really need add abs(v1) to get what I need
hope you can help me

Best wishes,
Can Jin

[FernandoRios]

Try q(25) instead of q(.25)

[Can Jin]

Try q(25) instead of q(.25)

thank you so much, Fernando
but it can not work

and I already solve this problem
just use stata15 SE
this code
mmqreg y x ,q(0.25) absorb(v1) nols
with abs(v1)
it can work in stata 15 SE
but can not work in stata 17 MP
I use stata 17 MP before

I do not know why
but it works

best wishes,
Can

[Minh Kha]

Try q(25) instead of q(.25)

Dear FernandoRios

According to what I've read in Prof. Santos Silva's research, the coefficient value of the scale cannot be negative, however, in reality, I'm still finding research papers with results where the location is a negative value, or a result right in the post also encounters this problem.

Code:
. use http://fmwww.bc.edu/RePEc/bocode/o/oaxaca.dta, clear (Excerpt from the Swiss Labor Market Survey 1998) . mmqreg lnwage female educ exper tenure , q(85 90) MM-qreg Estimator Number of obs = 1434 Quantile: . ------------------------------------------------------------------------------ lnwage | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- location | female | -.0841137 .0255318 -3.29 0.001 -.1341552 -.0340722 educ | .0847507 .0049257 17.21 0.000 .0750964 .094405 exper | .0110983 .0014531 7.64 0.000 .0082504 .0139463 tenure | .0077084 .0018578 4.15 0.000 .0040672 .0113496 _cons | 2.213327 .0682166 32.45 0.000 2.079625 2.347029 -------------+---------------------------------------------------------------- scale | female | .0579893 .0192994 3.00 0.003 .020163 .0958155 educ | -.0125849 .0037233 -3.38 0.001 -.0198826 -.0052873 exper | -.0054269 .0010984 -4.94 0.000 -.0075796 -.0032741 tenure | .0031301 .0014043 2.23 0.026 .0003778 .0058825 _cons | .4823967 .0515647 9.36 0.000 .3813318 .5834617 -------------+---------------------------------------------------------------- qtile_85 | female | -.0176419 .0253919 -0.69 0.487 -.0674091 .0321253 educ | .0703249 .0048994 14.35 0.000 .0607222 .0799275 exper | .0048776 .0014463 3.37 0.001 .0020429 .0077124 tenure | .0112964 .0018473 6.12 0.000 .0076759 .014917 _cons | 2.766287 .0683739 40.46 0.000 2.632277 2.900298 -------------+---------------------------------------------------------------- qtile_90 | female | -.0014663 .0283704 -0.05 0.959 -.0570713 .0541388 educ | .0668144 .005486 12.18 0.000 .056062 .0775668 exper | .0033638 .0016384 2.05 0.040 .0001525 .0065751 tenure | .0121696 .0020559 5.92 0.000 .0081401 .016199 _cons | 2.900848 .0805478 36.01 0.000 2.742977 3.058719 ------------------------------------------------------------------------------ . test [qtile_85]female = [qtile_90]female ( 1) [qtile_85]female - [qtile_90]female = 0 chi2( 1) = 7.29 Prob > chi2 = 0.0069 . lincom [qtile_85]female - [qtile_90]female ( 1) [qtile_85]female - [qtile_90]female = 0 ------------------------------------------------------------------------------ lnwage | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- (1) | -.0161756 .0059899 -2.70 0.007 -.0279157 -.0044356 ------------------------------------------------------------------------------ . . sqreg lnwage female educ exper tenure , q(85 90) (fitting base model) Bootstrap replications (20): .........10.........20 done Simultaneous quantile regression Number of obs = 1,434 bootstrap(20) SEs .85 Pseudo R2 = 0.1235 .90 Pseudo R2 = 0.1002 ------------------------------------------------------------------------------ | Bootstrap lnwage | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- q85 | female | -.048664 .0302942 -1.61 0.108 -.1080899 .0107618 educ | .0689975 .0040432 17.07 0.000 .0610663 .0769287 exper | .0050717 .0014436 3.51 0.000 .0022398 .0079035 tenure | .0099555 .0021046 4.73 0.000 .0058271 .0140838 _cons | 2.807041 .0780006 35.99 0.000 2.654033 2.960049 -------------+---------------------------------------------------------------- q90 | female | -.0542809 .038456 -1.41 0.158 -.1297171 .0211553 educ | .0639304 .0072212 8.85 0.000 .0497652 .0780957 exper | .0040191 .002412 1.67 0.096 -.0007124 .0087505 tenure | .0077403 .0026316 2.94 0.003 .002578 .0129027 _cons | 2.983513 .1189851 25.07 0.000 2.750109 3.216917 ------------------------------------------------------------------------------ . test [q85]female = [q90]female ( 1) [q85]female - [q90]female = 0 F( 1, 1429) = 0.06 Prob > F = 0.8084 . lincom [q85]female - [q90]female ( 1) [q85]female - [q90]female = 0 ------------------------------------------------------------------------------ lnwage | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- (1) | .0056169 .0231544 0.24 0.808 -.0398034 .0510371 ------------------------------------------------------------------------------ .
Am I misunderstanding something regarding the explanation of Santos Silva and Machado?

I'll wait for your response.

Best wishes,

Kha

[Joao Santos Silva]

Dear Minh Kha,

What cannot be negative is the fitted value of the scale function; there are no other restrictions on the coefficients.

Best wishes,

Joao

[Minh Kha]

Dear Joao Santos Silva,

Thanks so much for your response, it really help with our team's workflow.

Best wishes,

Kha