[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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