Hi,
I know that we must set the same seed for reproducible. For examples,
When the different seed is used, the results are significant difference. For example,
From the above results we can see that the coefficient of foreign is significant at 5% level(p = 0.047) when the seed is 1234.
However, the coefficient of foreign is not significant at 5% level(p = 0.076) when the seed is 125874124.
I understand that we cannot see the P value only. Since this question are raised by a reviewer.
However, how could I solve this problem in a more reasonable? Could I solve this problem by increasing the rep number?
Thanks very much for everyone kindly suggestions!
Bests,
wanhaiyou
I know that we must set the same seed for reproducible. For examples,
Code:
. sysuse auto,clear (1978 Automobile Data) . set seed 1234 . sqreg price weight length foreign, quantile(.25 .5 .75) reps(100) (fitting base model) (bootstrapping ....................................................................................................) Simultaneous quantile regression Number of obs = 74 bootstrap(100) SEs .25 Pseudo R2 = 0.1697 .50 Pseudo R2 = 0.2347 .75 Pseudo R2 = 0.3840 ------------------------------------------------------------------------------ | Bootstrap price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- q25 | weight | 1.831789 1.063354 1.72 0.089 -.2890044 3.952582 length | 2.845558 19.77565 0.14 0.886 -36.59573 42.28685 foreign | 2209.925 1095.034 2.02 0.047 25.94851 4393.902 _cons | -1879.775 2858.265 -0.66 0.513 -7580.405 3820.856 -------------+---------------------------------------------------------------- q50 | weight | 3.933588 2.477513 1.59 0.117 -1.007656 8.874832 length | -41.25191 63.16333 -0.65 0.516 -167.2272 84.72338 foreign | 3377.771 1270.405 2.66 0.010 844.0279 5911.514 _cons | 344.6494 5481.411 0.06 0.950 -10587.68 11276.98 -------------+---------------------------------------------------------------- q75 | weight | 9.22291 2.190276 4.21 0.000 4.854543 13.59128 length | -220.7833 75.01802 -2.94 0.004 -370.402 -71.16457 foreign | 3595.133 1124.303 3.20 0.002 1352.782 5837.484 _cons | 20242.9 8416.244 2.41 0.019 3457.236 37028.57 ------------------------------------------------------------------------------ . set seed 1234 . sqreg price weight length foreign, quantile(.25 .5 .75) reps(100) (fitting base model) (bootstrapping ....................................................................................................) Simultaneous quantile regression Number of obs = 74 bootstrap(100) SEs .25 Pseudo R2 = 0.1697 .50 Pseudo R2 = 0.2347 .75 Pseudo R2 = 0.3840 ------------------------------------------------------------------------------ | Bootstrap price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- q25 | weight | 1.831789 1.063354 1.72 0.089 -.2890044 3.952582 length | 2.845558 19.77565 0.14 0.886 -36.59573 42.28685 foreign | 2209.925 1095.034 2.02 0.047 25.94851 4393.902 _cons | -1879.775 2858.265 -0.66 0.513 -7580.405 3820.856 -------------+---------------------------------------------------------------- q50 | weight | 3.933588 2.477513 1.59 0.117 -1.007656 8.874832 length | -41.25191 63.16333 -0.65 0.516 -167.2272 84.72338 foreign | 3377.771 1270.405 2.66 0.010 844.0279 5911.514 _cons | 344.6494 5481.411 0.06 0.950 -10587.68 11276.98 -------------+---------------------------------------------------------------- q75 | weight | 9.22291 2.190276 4.21 0.000 4.854543 13.59128 length | -220.7833 75.01802 -2.94 0.004 -370.402 -71.16457 foreign | 3595.133 1124.303 3.20 0.002 1352.782 5837.484 _cons | 20242.9 8416.244 2.41 0.019 3457.236 37028.57 ------------------------------------------------------------------------------
Code:
. set seed 125874124
. sqreg price weight length foreign, quantile(.25 .5 .75) reps(100)
(fitting base model)
(bootstrapping ....................................................................................................)
Simultaneous quantile regression Number of obs = 74
bootstrap(100) SEs .25 Pseudo R2 = 0.1697
.50 Pseudo R2 = 0.2347
.75 Pseudo R2 = 0.3840
------------------------------------------------------------------------------
| Bootstrap
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q25 |
weight | 1.831789 1.662877 1.10 0.274 -1.484715 5.148293
length | 2.845558 35.83785 0.08 0.937 -68.63078 74.3219
foreign | 2209.925 1226.628 1.80 0.076 -236.5061 4656.357
_cons | -1879.775 3741.564 -0.50 0.617 -9342.088 5582.539
-------------+----------------------------------------------------------------
q50 |
weight | 3.933588 2.864569 1.37 0.174 -1.779615 9.646791
length | -41.25191 73.95145 -0.56 0.579 -188.7434 106.2396
foreign | 3377.771 1211.897 2.79 0.007 960.7191 5794.823
_cons | 344.6494 6359.924 0.05 0.957 -12339.82 13029.12
-------------+----------------------------------------------------------------
q75 |
weight | 9.22291 2.473674 3.73 0.000 4.289322 14.1565
length | -220.7833 82.6403 -2.67 0.009 -385.6042 -55.9624
foreign | 3595.133 1114.196 3.23 0.002 1372.939 5817.328
_cons | 20242.9 8790.816 2.30 0.024 2710.174 37775.63
------------------------------------------------------------------------------
However, the coefficient of foreign is not significant at 5% level(p = 0.076) when the seed is 125874124.
I understand that we cannot see the P value only. Since this question are raised by a reviewer.
However, how could I solve this problem in a more reasonable? Could I solve this problem by increasing the rep number?
Thanks very much for everyone kindly suggestions!
Bests,
wanhaiyou
Comment