[Limdep Nlogit List] Weighting in quantile regression

Alessandro Corsi alessandro.corsi at unito.it
Sat Mar 5 03:39:39 AEDT 2022


Does the WTS; specification work with the QREG; model, and with the 
Quantiles; command? (I am using Nlogit 6) The WTS specification seems to 
produce no effect on the estimation.

I run the following:     QREG;Lhs=meatprot; Rhs=one,gdppc;quantile=.25 $ 
(output below)

and obtained exactly the same output with:   QREG;Lhs=meatprot; 
Rhs=one,gdppc;quantile=.25; wts= populati $

(though it signals a weighting variable, see below)

Moreover, when checking the data with: QUANTILES;  rhs=meatprot$

I got exactly the same results as with: QUANTILES; rhs=meatprot; wts= 
populati $

Thanks for clarifications

Alessandro Corsi


QREG;Lhs=meatprot; Rhs=one,gdppc;quantile=.25 $

-----------------------------------------------------------------------------
Quantile Regression Model. Quantile =      .250000
Linear Programming estimation method
LHS=MEATPROT Mean                 =       16.28292
              Standard deviation   =       10.97889
              Number of observs.   =           2296
              Minimum              =        1.16000
              t= .25000 quantile   =        6.29000
              Maximum              =       46.93000
Model size   Parameters           =              2
              Degrees of freedom   =           2294
Residuals    Sum of squares       =   160762.75237
              Standard error of e  =        7.06346
Fit          R-squared            =         .58608
              PseudoR2=1-F(0)/F(b) =         .37028
Not using OLS or no constant. Rsquared may be <= 0
Functions F= Sum r(t)[y(i)-x(i)b] =     4459.21969
           F0=Sum r(t)[y(i)-Qy(t)] =     7081.28500
              r(t)[u]=t*u-u*[u<0].t=        .250000
Asymptotic cov. matrix based on  kernel estimator.
Heteroscedasticity test, Chi2[ 1] =237.11 P = .000
--------+--------------------------------------------------------------------
         |                  Standard            Prob.      95% Confidence
MEATPROT|  Coefficient       Error       z    |z|>Z* Interval
--------+--------------------------------------------------------------------
Constant|    3.27656***      .20597    15.91  .0000 2.87286   3.68026
    GDPPC|     .00048***   .9048D-05    53.21  .0000 .00046    .00050
--------+--------------------------------------------------------------------
nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx.
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Mar 04, 2022 at 04:43:31 PM
-----------------------------------------------------------------------------

-----------------------------------------------------------------------------
Quantile Regression Model. Quantile =      .250000
Linear Programming estimation method
LHS=MEATPROT Mean                 =       16.28292
              Standard deviation   =       10.97889
WTS=POPULATI Number of observs.   =           2296
              Minimum              =        1.16000
              t= .25000 quantile   =        6.29000
              Maximum              =       46.93000
Model size   Parameters           =              2
              Degrees of freedom   =           2294
Residuals    Sum of squares       =   160762.75237
              Standard error of e  =        7.06346
Fit          R-squared            =         .58608
              PseudoR2=1-F(0)/F(b) =         .37028
Not using OLS or no constant. Rsquared may be <= 0
Functions F= Sum r(t)[y(i)-x(i)b] =     4459.21969
           F0=Sum r(t)[y(i)-Qy(t)] =     7081.28500
              r(t)[u]=t*u-u*[u<0].t=        .250000
Asymptotic cov. matrix based on  kernel estimator.
Heteroscedasticity test, Chi2[ 1] =237.11 P = .000
--------+--------------------------------------------------------------------
         |                  Standard            Prob.      95% Confidence
MEATPROT|  Coefficient       Error       z    |z|>Z* Interval
--------+--------------------------------------------------------------------
Constant|    3.27656***      .20597    15.91  .0000 2.87286   3.68026
    GDPPC|     .00048***   .9048D-05    53.21  .0000 .00046    .00050
--------+--------------------------------------------------------------------
nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx.
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Mar 04, 2022 at 04:43:59 PM
-----------------------------------------------------------------------------


QREG;Lhs=meatprot; Rhs=one,gdppc;quantile=.25; wts= populati $

-----------------------------------------------------------------------------
Quantile Regression Model. Quantile =      .250000
Linear Programming estimation method
LHS=MEATPROT Mean                 =       16.28292
              Standard deviation   =       10.97889
WTS=POPULATI Number of observs.   =           2296
              Minimum              =        1.16000
              t= .25000 quantile   =        6.29000
              Maximum              =       46.93000
Model size   Parameters           =              2
              Degrees of freedom   =           2294
Residuals    Sum of squares       =   160762.75237
              Standard error of e  =        7.06346
Fit          R-squared            =         .58608
              PseudoR2=1-F(0)/F(b) =         .37028
Not using OLS or no constant. Rsquared may be <= 0
Functions F= Sum r(t)[y(i)-x(i)b] =     4459.21969
           F0=Sum r(t)[y(i)-Qy(t)] =     7081.28500
              r(t)[u]=t*u-u*[u<0].t=        .250000
Asymptotic cov. matrix based on  kernel estimator.
Heteroscedasticity test, Chi2[ 1] =237.11 P = .000
--------+--------------------------------------------------------------------
         |                  Standard            Prob.      95% Confidence
MEATPROT|  Coefficient       Error       z    |z|>Z* Interval
--------+--------------------------------------------------------------------
Constant|    3.27656***      .20597    15.91  .0000 2.87286   3.68026
    GDPPC|     .00048***   .9048D-05    53.21  .0000 .00046    .00050
--------+--------------------------------------------------------------------
nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx.
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Mar 04, 2022 at 04:43:59 PM
-----------------------------------------------------------------------------

-----------------------------------------------------------------------------
Quantile Regression Model. Quantile =      .250000
Linear Programming estimation method
LHS=MEATPROT Mean                 =       16.28292
              Standard deviation   =       10.97889
WTS=POPULATI Number of observs.   =           2296
              Minimum              =        1.16000
              t= .25000 quantile   =        6.29000
              Maximum              =       46.93000
Model size   Parameters           =              2
              Degrees of freedom   =           2294
Residuals    Sum of squares       =   160762.75237
              Standard error of e  =        7.06346
Fit          R-squared            =         .58608
              PseudoR2=1-F(0)/F(b) =         .37028
Not using OLS or no constant. Rsquared may be <= 0
Functions F= Sum r(t)[y(i)-x(i)b] =     4459.21969
           F0=Sum r(t)[y(i)-Qy(t)] =     7081.28500
              r(t)[u]=t*u-u*[u<0].t=        .250000
Asymptotic cov. matrix based on  kernel estimator.
Heteroscedasticity test, Chi2[ 1] =237.11 P = .000
--------+--------------------------------------------------------------------
         |                  Standard            Prob.      95% Confidence
MEATPROT|  Coefficient       Error       z    |z|>Z* Interval
--------+--------------------------------------------------------------------
Constant|    3.27656***      .20597    15.91  .0000 2.87286   3.68026
    GDPPC|     .00048***   .9048D-05    53.21  .0000 .00046    .00050
--------+--------------------------------------------------------------------
nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx.
***, **, * ==>  Significance at 1%, 5%, 10% level.
Model was estimated on Mar 04, 2022 at 04:43:59 PM
-----------------------------------------------------------------------------



Moreover, when checking the data with: QUANTILES;  rhs=meatprot$

I got exactly the same results as with: QUANTILES; rhs=meatprot; wts= 
populati $

Thanks for clarifications

Alessandro Corsi


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