Dear all,
I´m working with a data set of firms to study barriers to innovation and I am using the qregpd to run a quantile regression with panel data to asses the effect of barriers in different points of the distribution of the varaible of interest (ln of labor productivity in this case). I´ve decided to go with the mcmc optimization method given that, even specifying a seed, I can´t seem to get the same result twice in a row using the default Nelder-Mead method. Here´s the output using Nelder-Mead algorithm.
Given these different results with this method I switched to MCMC in the belief that in this case the seed would work. However, using this method I have the problem that different seeds return different regression results!
I´m getting different point estimates and different statistical significance depending on the seed used. Anybody knows how to work this out?
Thank you.
Daniel
I´m working with a data set of firms to study barriers to innovation and I am using the qregpd to run a quantile regression with panel data to asses the effect of barriers in different points of the distribution of the varaible of interest (ln of labor productivity in this case). I´ve decided to go with the mcmc optimization method given that, even specifying a seed, I can´t seem to get the same result twice in a row using the default Nelder-Mead method. Here´s the output using Nelder-Mead algorithm.
Code:
set seed 2003080
qregpd lnprod $barreras exporter2_ lneduc lnedad lnsize if relevant4==1, id(correlativo) fix(periodo) q(0.10)
Quantile Regression for Panel Data (QRPD)
Number of obs: 1941
Number of groups: 504
Min obs per group: 3
Max obs per group: 4
------------------------------------------------------------------------------
lnprod | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cost_ | -.021396 .1942694 -0.11 0.912 -.402157 .359365
knowledge_ | -.0164477 .1071803 -0.15 0.878 -.2265173 .1936219
market_ | .0021737 .2487624 0.01 0.993 -.4853917 .4897391
reg_ | .011481 .23233 0.05 0.961 -.4438775 .4668395
exporter2_ | .0027602 .0048785 0.57 0.572 -.0068016 .012322
lneduc | .1743128 .2203763 0.79 0.429 -.2576168 .6062424
lnedad | 9.628899 17.29708 0.56 0.578 -24.27275 43.53055
lnsize | .2744016 .9562956 0.29 0.774 -1.599903 2.148707
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
set seed 2003080
qregpd lnprod $barreras exporter2_ lneduc lnedad lnsize if relevant4==1, id(correlativo) fix(periodo) q(0.10)
Quantile Regression for Panel Data (QRPD)
Number of obs: 1941
Number of groups: 504
Min obs per group: 3
Max obs per group: 4
------------------------------------------------------------------------------
lnprod | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cost_ | -.0210913 .0918877 -0.23 0.818 -.2011879 .1590053
knowledge_ | -.0158104 .0920948 -0.17 0.864 -.1963129 .1646922
market_ | .0027017 .1191696 0.02 0.982 -.2308663 .2362698
reg_ | .0100679 .1204593 0.08 0.933 -.2260281 .2461638
exporter2_ | .0028732 .0032588 0.88 0.378 -.0035141 .0092604
lneduc | .1740414 .1397286 1.25 0.213 -.0998215 .4479044
lnedad | 9.628627 1.908467 5.05 0.000 5.888101 13.36915
lnsize | .2744354 .2059562 1.33 0.183 -.1292313 .6781022
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
Code:
. set seed 2003080
. qregpd lnprod $barreras sNI_ exporter2_ lneduc lnedad lnsize if relevant4==1, id(correlativo) fix(periodo) q(0.1) optimize(mcmc)
> draws(1000) burn(100)
Adaptive MCMC optimization
Quantile Regression for Panel Data (QRPD)
Number of obs: 1941
Number of groups: 504
Min obs per group: 3
Max obs per group: 4
------------------------------------------------------------------------------
lnprod | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cost_ | -.022124 .0353219 -0.63 0.531 -.0913537 .0471057
knowledge_ | -.0123532 .0376526 -0.33 0.743 -.0861508 .0614445
market_ | .0285633 .037407 0.76 0.445 -.0447531 .1018797
reg_ | -.0183071 .0387575 -0.47 0.637 -.0942704 .0576562
sNI_ | -.0879371 .042883 -2.05 0.040 -.1719862 -.0038879
exporter2_ | .0007951 .0031179 0.26 0.799 -.0053159 .0069062
lneduc | .0563408 .0421624 1.34 0.181 -.026296 .1389776
lnedad | .9033293 .0846333 10.67 0.000 .7374509 1.069208
lnsize | -.3157141 .0479072 -6.59 0.000 -.4096104 -.2218178
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
MCMC diagonstics:
Mean acceptance rate: 0.222
Total draws: 1000
Burn-in draws: 100
Draws retained: 900
Value of objective function:
Mean: -7.7385
Min: -16.7594
Max: -3.2065
MCMC notes:
*Point estimates correspond to mean of draws.
*Standard errors are derived from variance of draws.
. set seed 2003081
. qregpd lnprod $barreras sNI_ exporter2_ lneduc lnedad lnsize if relevant4==1, id(correlativo) fix(periodo) q(0.1) optimize(mcmc)
> draws(1000) burn(100)
Adaptive MCMC optimization
Quantile Regression for Panel Data (QRPD)
Number of obs: 1941
Number of groups: 504
Min obs per group: 3
Max obs per group: 4
------------------------------------------------------------------------------
lnprod | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cost_ | -.0294718 .0269462 -1.09 0.274 -.0822853 .0233417
knowledge_ | .0092274 .0537253 0.17 0.864 -.0960723 .1145271
market_ | .0100021 .0320289 0.31 0.755 -.0527734 .0727776
reg_ | .103807 .0354037 2.93 0.003 .0344171 .1731968
sNI_ | .065963 .0420626 1.57 0.117 -.0164782 .1484042
exporter2_ | .0095051 .0021575 4.41 0.000 .0052765 .0137337
lneduc | .1172342 .0172016 6.82 0.000 .0835196 .1509487
lnedad | .8451934 .0497182 17.00 0.000 .7477474 .9426393
lnsize | -.3524431 .0246028 -14.33 0.000 -.4006638 -.3042224
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
MCMC diagonstics:
Mean acceptance rate: 0.251
Total draws: 1000
Burn-in draws: 100
Draws retained: 900
Value of objective function:
Mean: -15.9054
Min: -23.2636
Max: -7.5322
MCMC notes:
*Point estimates correspond to mean of draws.
*Standard errors are derived from variance of draws.
. set seed 2003082
. qregpd lnprod $barreras sNI_ exporter2_ lneduc lnedad lnsize if relevant4==1, id(correlativo) fix(periodo) q(0.1) optimize(mcmc)
> draws(1000) burn(100)
Adaptive MCMC optimization
Quantile Regression for Panel Data (QRPD)
Number of obs: 1941
Number of groups: 504
Min obs per group: 3
Max obs per group: 4
------------------------------------------------------------------------------
lnprod | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cost_ | -.0710054 .0614428 -1.16 0.248 -.191431 .0494203
knowledge_ | -.0234029 .0287051 -0.82 0.415 -.0796638 .032858
market_ | -.1040086 .0441818 -2.35 0.019 -.1906034 -.0174139
reg_ | .0412002 .0355504 1.16 0.246 -.0284772 .1108777
sNI_ | .0229435 .044611 0.51 0.607 -.0644924 .1103793
exporter2_ | .0112458 .0010849 10.37 0.000 .0091195 .0133722
lneduc | .130359 .0106865 12.20 0.000 .109414 .1513041
lnedad | .6781942 .0353308 19.20 0.000 .608947 .7474414
lnsize | -.3386571 .0225564 -15.01 0.000 -.3828668 -.2944474
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
MCMC diagonstics:
Mean acceptance rate: 0.222
Total draws: 1000
Burn-in draws: 100
Draws retained: 900
Value of objective function:
Mean: -20.2498
Min: -35.5141
Max: -14.9407
MCMC notes:
*Point estimates correspond to mean of draws.
*Standard errors are derived from variance of draws.
Thank you.
Daniel
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