I have data on income and want to calculate percentiles using bootstrapping as well as population weights. What I did so far (and what worked):
collapse (p10) p10 = income ///
(p90) p90 = income ///
(p95) p95 = income ///
(p99) p99 = income ///
[iweight = wg], by(year)
How can I add bootstrapping to this code?
Thanks!
There is a community-contributed command qrprocess that can do fast multiple quantile regression with weights and bootstrapping:
. sysuse auto, clear
(1978 automobile data)
. drop if missing(rep78)
(5 observations deleted)
. set seed 10312023
. ssc install moremata
checking moremata consistency and verifying not already installed...
all files already exist and are up to date.
. ssc install qrprocess
checking qrprocess consistency and verifying not already installed...
all files already exist and are up to date.
. /* M1: use weighted quantile regression + margins get the quantiles */
. qrprocess price i.rep78 [iw=mpg], q(.10 .90 .95 .99) vce(bootstrap, reps(100))
(bootstrapping .x.......x......x.......x................................x.................x.........x.x....x..x..............)
Quantile regression
No. of obs. 69
Algorithm: qreg.
Variance: empirical bootstrap.
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Quant. 0.1 |
2.rep78 | -528 460.1462 -1.15 0.255 -1447.248 391.2477
3.rep78 | -300 423.3904 -0.71 0.481 -1145.82 545.8195
4.rep78 | -200 458.3257 -0.44 0.664 -1115.611 715.6108
5.rep78 | -447 464.7086 -0.96 0.340 -1375.362 481.3622
_cons | 4195 333.4818 12.58 0.000 3528.794 4861.206
------------------------------------------------------------------------------
Quant. 0.9 |
2.rep78 | 1408 4199.176 0.34 0.738 -6980.819 9796.819
3.rep78 | 6563 2324.565 2.82 0.006 1919.148 11206.85
4.rep78 | 3880 992.606 3.91 0.000 1897.042 5862.958
5.rep78 | 4756 2737.756 1.74 0.087 -713.2964 10225.3
_cons | 4934 333.4818 14.80 0.000 4267.794 5600.206
------------------------------------------------------------------------------
Quant. 0.95 |
2.rep78 | 9566 4110.508 2.33 0.023 1354.317 17777.68
3.rep78 | 8660 2004.501 4.32 0.000 4655.548 12664.45
4.rep78 | 4801 747.2811 6.42 0.000 3308.134 6293.866
5.rep78 | 7061 2410.464 2.93 0.005 2245.545 11876.46
_cons | 4934 333.4818 14.80 0.000 4267.794 5600.206
------------------------------------------------------------------------------
Quant. 0.99 |
2.rep78 | 9566 4032.689 2.37 0.021 1509.778 17622.22
3.rep78 | 10972 1289.523 8.51 0.000 8395.882 13548.12
4.rep78 | 4801 717.3175 6.69 0.000 3367.993 6234.007
5.rep78 | 7061 2024.685 3.49 0.001 3016.226 11105.77
_cons | 4934 333.4818 14.80 0.000 4267.794 5600.206
------------------------------------------------------------------------------
. margins rep78 [iw=mpg], predict(equation(q1)) predict(equation(q2)) predict(equation(q3)) predict(equation(q4))
warning: cannot perform check for estimable functions.
Adjusted predictions Number of obs = 69
Model VCE: BOOTSTRAP
1._predict: 0..1 QR fitted values, predict(equation(q1))
2._predict: 0..9 QR fitted values, predict(equation(q2))
3._predict: 0..95 QR fitted values, predict(equation(q3))
4._predict: 0..99 QR fitted values, predict(equation(q4))
--------------------------------------------------------------------------------
| Delta-method
| Margin std. err. z P>|z| [95% conf. interval]
---------------+----------------------------------------------------------------
_predict#rep78 |
1 1 | 4195 333.4818 12.58 0.000 3541.388 4848.612
1 2 | 3667 303.8006 12.07 0.000 3071.562 4262.438
1 3 | 3895 319.6076 12.19 0.000 3268.581 4521.419
1 4 | 3995 333.1012 11.99 0.000 3342.134 4647.866
1 5 | 3748 272.9058 13.73 0.000 3213.115 4282.885
2 1 | 4934 333.4818 14.80 0.000 4280.388 5587.612
2 2 | 6342 4197.791 1.51 0.131 -1885.519 14569.52
2 3 | 11497 2298.64 5.00 0.000 6991.747 16002.25
2 4 | 8814 889.7568 9.91 0.000 7070.109 10557.89
2 5 | 9690 2660.416 3.64 0.000 4475.681 14904.32
3 1 | 4934 333.4818 14.80 0.000 4280.388 5587.612
3 2 | 14500 4100.996 3.54 0.000 6462.196 22537.8
3 3 | 13594 1975.281 6.88 0.000 9722.521 17465.48
3 4 | 9735 673.6134 14.45 0.000 8414.742 11055.26
3 5 | 11995 2362.492 5.08 0.000 7364.6 16625.4
4 1 | 4934 333.4818 14.80 0.000 4280.388 5587.612
4 2 | 14500 4021.214 3.61 0.000 6618.566 22381.43
4 3 | 15906 1240.245 12.82 0.000 13475.16 18336.84
4 4 | 9735 639.6546 15.22 0.000 8481.3 10988.7
4 5 | 11995 1983.466 6.05 0.000 8107.478 15882.52
--------------------------------------------------------------------------------
. /* M2: confirm output of margins matches your code */
. collapse ///
> (p10) p10 = price ///
> (p90) p90 = price ///
> (p95) p95 = price ///
> (p99) p99 = price ///
> [iweight = mpg], by(rep78)
. list, noobs
+------------------------------------------+
| rep78 p10 p90 p95 p99 |
|------------------------------------------|
| 1 4,195 4,934 4,934 4,934 |
| 2 3,667 6,342 14,500 14,500 |
| 3 3,895 11,497 13,594 15,906 |
| 4 3,995 8,814 9,735 9,735 |
| 5 3,748 9,690 11,995 11,995 |
+------------------------------------------+