************************************************************************
clear all
capture program drop mc
program mc
drop _all
set obs 10000
gen x1 = runiform()
gen x2 = runiform()
gen u = 1*rnormal()
gen y = 0 + 1*x1 /* + -1*x2 */ + u
regress y x1
end
mc
simulate mc _b _se, reps(1000)
*************************************************************************************
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 10
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 2*rnormal()
6.
. gen y = 0 + 1*x1 + -1*x2 + u
7.
. gen y x1 x2
8. end
.
. mc
obs was 0, now 10
y already defined
r(110);
end of do-file
r(110);
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 10
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 2*rnormal()
6.
. gen y = 0 + 1*x1 + -1*x2 + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 10
Source | SS df MS Number of obs = 10
-------------+------------------------------ F( 2, 7) = 0.02
Model | .430908682 2 .215454341 Prob > F = 0.9793
Residual | 71.7952867 7 10.2564695 R-squared = 0.0060
-------------+------------------------------ Adj R-squared = -0.2780
Total | 72.2261954 9 8.02513282 Root MSE = 3.2026
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -.5085118 3.430882 -0.15 0.886 -8.621259 7.604235
x2 | -.8432807 4.789542 -0.18 0.865 -12.16875 10.48219
_cons | .3517102 3.063205 0.11 0.912 -6.89162 7.59504
------------------------------------------------------------------------------
.
end of do-file
. mc
obs was 0, now 10
Source | SS df MS Number of obs = 10
-------------+------------------------------ F( 2, 7) = 2.09
Model | 15.4575553 2 7.72877766 Prob > F = 0.1940
Residual | 25.8643663 7 3.69490947 R-squared = 0.3741
-------------+------------------------------ Adj R-squared = 0.1952
Total | 41.3219216 9 4.59132463 Root MSE = 1.9222
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -4.113555 2.658621 -1.55 0.166 -10.40019 2.173085
x2 | -4.654333 2.655914 -1.75 0.123 -10.93457 1.625906
_cons | 5.145695 2.352045 2.19 0.065 -.4160074 10.7074
------------------------------------------------------------------------------
. mc
obs was 0, now 10
Source | SS df MS Number of obs = 10
-------------+------------------------------ F( 2, 7) = 0.15
Model | 1.50772343 2 .753861717 Prob > F = 0.8636
Residual | 35.2273974 7 5.03248535 R-squared = 0.0410
-------------+------------------------------ Adj R-squared = -0.2329
Total | 36.7351209 9 4.0816801 Root MSE = 2.2433
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .4254098 2.580488 0.16 0.874 -5.676475 6.527295
x2 | -1.663124 3.088707 -0.54 0.607 -8.966756 5.640508
_cons | .5678954 2.35712 0.24 0.817 -5.005807 6.141598
------------------------------------------------------------------------------
. mc
obs was 0, now 10
Source | SS df MS Number of obs = 10
-------------+------------------------------ F( 2, 7) = 0.01
Model | .157879045 2 .078939523 Prob > F = 0.9888
Residual | 49.0571035 7 7.00815764 R-squared = 0.0032
-------------+------------------------------ Adj R-squared = -0.2816
Total | 49.2149825 9 5.46833139 Root MSE = 2.6473
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -.5340727 4.318352 -0.12 0.905 -10.74535 9.677208
x2 | .5947896 4.016886 0.15 0.886 -8.903637 10.09322
_cons | .6182353 1.763762 0.35 0.736 -3.552398 4.788869
------------------------------------------------------------------------------
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. simulate mc _b _se, reps(10)
command: mc
statistics: b_x1 = _b[x1]
b_x2 = _b[x2]
b_cons = _b[_cons]
se_x1 = _se[x1]
se_x2 = _se[x2]
se_cons = _se[_cons]
.
end of do-file
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 10
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = rnormal()
6.
. gen y = 0 + 1*x1 + -1*x2 + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 10
Source | SS df MS Number of obs = 10
-------------+------------------------------ F( 2, 7) = 0.27
Model | .82482621 2 .412413105 Prob > F = 0.7705
Residual | 10.6656976 7 1.52367109 R-squared = 0.0718
-------------+------------------------------ Adj R-squared = -0.1934
Total | 11.4905238 9 1.27672487 Root MSE = 1.2344
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .7285759 1.734065 0.42 0.687 -3.371837 4.828989
x2 | .5195387 1.465888 0.35 0.733 -2.946735 3.985813
_cons | -.1477854 .8659571 -0.17 0.869 -2.195449 1.899878
------------------------------------------------------------------------------
.
end of do-file
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 10
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 0.1*rnormal()
6.
. gen y = 0 + 1*x1 + -1*x2 + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 10
Source | SS df MS Number of obs = 10
-------------+------------------------------ F( 2, 7) = 20.39
Model | .211536546 2 .105768273 Prob > F = 0.0012
Residual | .036308211 7 .005186887 R-squared = 0.8535
-------------+------------------------------ Adj R-squared = 0.8116
Total | .247844757 9 .027538306 Root MSE = .07202
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .740921 .1195578 6.20 0.000 .4582117 1.02363
x2 | -.7461229 .130325 -5.73 0.001 -1.054293 -.4379533
_cons | .0413552 .0533436 0.78 0.464 -.0847824 .1674928
------------------------------------------------------------------------------
.
end of do-file
. twoway scatter y u
. twoway scatter y x1
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 0.1*rnormal()
6.
. gen y = 0 + 1*x1 + -1*x2 + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 2, 97) = 782.51
Model | 17.6356947 2 8.81784736 Prob > F = 0.0000
Residual | 1.09305861 97 .011268645 R-squared = 0.9416
-------------+------------------------------ Adj R-squared = 0.9404
Total | 18.7287533 99 .189179327 Root MSE = .10615
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1.025326 .0349122 29.37 0.000 .9560351 1.094617
x2 | -1.010782 .0410366 -24.63 0.000 -1.092228 -.9293353
_cons | -.0134146 .0283046 -0.47 0.637 -.0695914 .0427622
------------------------------------------------------------------------------
.
end of do-file
. twoway scatter y x1
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 0.1*rnormal()
6.
. gen y = 0 + 1*x1 + -1*x2 // + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 2, 97) = .
Model | 18.1943824 2 9.09719122 Prob > F = .
Residual | 0 97 0 R-squared = 1.0000
-------------+------------------------------ Adj R-squared = 1.0000
Total | 18.1943824 99 .183781641 Root MSE = 0
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1 . . . . .
x2 | -1 . . . . .
_cons | -2.52e-09 . . . . .
------------------------------------------------------------------------------
.
end of do-file
. twoway scatter y x1
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 0.1*rnormal()
6.
. gen y = 0 + 1*x1 // + -1*x2 // + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 2, 97) = .
Model | 8.93699606 2 4.46849803 Prob > F = .
Residual | 0 97 0 R-squared = 1.0000
-------------+------------------------------ Adj R-squared = 1.0000
Total | 8.93699606 99 .090272688 Root MSE = 0
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1 . . . . .
x2 | 2.58e-18 . . . . .
_cons | -1.11e-16 . . . . .
------------------------------------------------------------------------------
.
. simulate mc _b _se, reps(1000)
command: mc
statistics: b_x1 = _b[x1]
b_x2 = _b[x2]
b_cons = _b[_cons]
se_x1 = _se[x1]
se_x2 = _se[x2]
se_cons = _se[_cons]
.
end of do-file
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 0.1*rnormal()
6.
. gen y = 0 + 1*x1 // + -1*x2 // + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 2, 97) = .
Model | 9.34278073 2 4.67139036 Prob > F = .
Residual | 0 97 0 R-squared = 1.0000
-------------+------------------------------ Adj R-squared = 1.0000
Total | 9.34278073 99 .094371523 Root MSE = 0
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1 . . . . .
x2 | -1.36e-17 . . . . .
_cons | -1.11e-16 . . . . .
------------------------------------------------------------------------------
.
end of do-file
. twoway scatter y x1
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 0.1*rnormal()
6.
. gen y = 0 + 1*x1 /* + -1*x2 */ + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 2, 97) = 391.01
Model | 7.39564433 2 3.69782217 Prob > F = 0.0000
Residual | .917338649 97 .009457099 R-squared = 0.8896
-------------+------------------------------ Adj R-squared = 0.8874
Total | 8.31298298 99 .083969525 Root MSE = .09725
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .9330553 .033609 27.76 0.000 .8663508 .9997598
x2 | -.008106 .0335827 -0.24 0.810 -.0747583 .0585464
_cons | .0469489 .026236 1.79 0.077 -.0051224 .0990201
------------------------------------------------------------------------------
.
end of do-file
. twoway scatter y x1
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 2*rnormal()
6.
. gen y = 0 + 1*x1 /* + -1*x2 */ + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 2, 97) = 0.12
Model | 1.0403493 2 .520174652 Prob > F = 0.8893
Residual | 429.647132 97 4.42935188 R-squared = 0.0024
-------------+------------------------------ Adj R-squared = -0.0182
Total | 430.687482 99 4.3503786 Root MSE = 2.1046
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -.2827588 .7432034 -0.38 0.704 -1.757812 1.192294
x2 | -.1737603 .7003509 -0.25 0.805 -1.563763 1.216243
_cons | 1.289489 .5365054 2.40 0.018 .2246747 2.354304
------------------------------------------------------------------------------
.
end of do-file
. twoway scatter y x1
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 1*rnormal()
6.
. gen y = 0 + 1*x1 /* + -1*x2 */ + u
7.
. regress y x1 x2
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 2, 97) = 0.74
Model | 1.24654997 2 .623274987 Prob > F = 0.4802
Residual | 81.800295 97 .84330201 R-squared = 0.0150
-------------+------------------------------ Adj R-squared = -0.0053
Total | 83.0468449 99 .83885702 Root MSE = .91831
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .3947887 .3272119 1.21 0.231 -.2546364 1.044214
x2 | .0526266 .3495082 0.15 0.881 -.6410505 .7463037
_cons | .3276033 .2610355 1.26 0.212 -.1904798 .8456865
------------------------------------------------------------------------------
.
end of do-file
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 100
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 1*rnormal()
6.
. gen y = 0 + 1*x1 /* + -1*x2 */ + u
7.
. regress y x1
8. end
.
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 1, 98) = 18.11
Model | 17.759309 1 17.759309 Prob > F = 0.0000
Residual | 96.0775547 98 .980383211 R-squared = 0.1560
-------------+------------------------------ Adj R-squared = 0.1474
Total | 113.836864 99 1.14986731 Root MSE = .99014
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1.544861 .3629729 4.26 0.000 .8245527 2.265168
_cons | -.273181 .1876893 -1.46 0.149 -.6456444 .0992823
------------------------------------------------------------------------------
.
end of do-file
. mc
obs was 0, now 100
Source | SS df MS Number of obs = 100
-------------+------------------------------ F( 1, 98) = 3.16
Model | 2.77529608 1 2.77529608 Prob > F = 0.0788
Residual | 86.2016582 98 .879608757 R-squared = 0.0312
-------------+------------------------------ Adj R-squared = 0.0213
Total | 88.9769543 99 .898757114 Root MSE = .93787
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .5846617 .3291507 1.78 0.079 -.0685271 1.23785
_cons | .3973983 .1766448 2.25 0.027 .0468526 .7479441
------------------------------------------------------------------------------
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
.
. simulate mc _b _se, reps(1000)
command: mc
statistics: b_x1 = _b[x1]
b_cons = _b[_cons]
se_x1 = _se[x1]
se_cons = _se[_cons]
.
end of do-file
. l
+---------------------------------------------+
| b_x1 b_cons se_x1 se_cons |
|---------------------------------------------|
1. | 1.069891 .2766387 .3725444 .1921761 |
2. | 1.141973 .0153552 .3806391 .2178938 |
3. | 1.114373 -.009886 .4139515 .2280637 |
4. | 1.642154 -.317777 .3415303 .2136485 |
5. | 1.035717 -.261528 .3854763 .2267857 |
|---------------------------------------------|
6. | .6829489 .188852 .3878753 .2174888 |
7. | 1.276004 -.1630904 .3354255 .1957426 |
8. | .85077 .1607194 .3143457 .189731 |
9. | .8176089 .1491402 .3201928 .17003 |
10. | .757905 -.032821 .3591557 .2070532 |
|---------------------------------------------|
11. | .7486533 -.0055785 .3262384 .1809447 |
12. | .7827135 .1093722 .3598103 .2049601 |
13. | 1.132485 -.2750028 .3016264 .1817723 |
14. | 1.560882 -.0599026 .3340715 .1905815 |
15. | .9720295 .1555404 .3531543 .2093485 |
|---------------------------------------------|
16. | 1.286714 -.0490981 .2921792 .1862092 |
17. | .7240109 .3340893 .3771952 .2287315 |
18. | .9786509 .1203361 .374947 .2105826 |
19. | .5682673 .3232818 .3210573 .1889068 |
20. | 1.246671 -.1217443 .3374139 .205506 |
|---------------------------------------------|
21. | .3577456 .403231 .3210521 .1924347 |
22. | .9304754 .1363738 .3240152 .1665811 |
23. | .3937671 .5464501 .3378707 .2093663 |
24. | 1.042597 .16413 .3932746 .2411786 |
25. | .5117619 .2869657 .3358699 .2126193 |
|---------------------------------------------|
26. | 1.038259 -.1832555 .3253127 .1949113 |
27. | .6637596 .1410158 .3389887 .1937855 |
28. | 1.31316 -.2338058 .3459665 .1977798 |
29. | 1.119853 -.0245571 .3494658 .2031568 |
30. | .7061825 .1243362 .3690408 .2119462 |
|---------------------------------------------|
31. | 1.042801 .0196157 .4024177 .2041931 |
32. | 1.488788 -.3459565 .3437817 .2034868 |
33. | .914715 .1253864 .3969534 .2145537 |
34. | 1.035796 .0024213 .362767 .2064709 |
35. | .8886128 .2146451 .3627798 .2087299 |
|---------------------------------------------|
36. | 1.18852 .0441686 .3131844 .1841896 |
37. | 1.20393 -.1176122 .3791897 .2155766 |
38. | .6190941 .1973605 .3903393 .2164128 |
39. | 1.215476 .1315013 .3529554 .2046187 |
40. | 1.083804 -.3119109 .3300541 .1887915 |
|---------------------------------------------|
41. | .8155182 .101609 .3172344 .1898869 |
42. | .9516908 .007275 .326588 .1833335 |
43. | .3948213 .1538598 .3816782 .1931216 |
44. | 1.094481 .0171842 .3188854 .1788529 |
45. | 1.299641 -.1624602 .3430224 .2051781 |
|---------------------------------------------|
46. | 1.374735 -.2267276 .3269438 .1747144 |
47. | 1.499057 -.2011559 .3458241 .1962487 |
48. | 1.098974 .1052785 .3451995 .206559 |
49. | 1.356845 -.1241643 .3039085 .1758098 |
50. | .6767249 .1548072 .3480919 .2108894 |
|---------------------------------------------|
51. | 1.185248 -.0605891 .410503 .2462644 |
52. | 1.122355 -.1529294 .3226524 .1806391 |
53. | .7633085 .0721199 .3358322 .1828511 |
54. | 1.108431 -.0567204 .3725818 .2136012 |
55. | .9837333 .1694459 .3519674 .1793028 |
|---------------------------------------------|
56. | 1.188589 -.1454761 .3666778 .2167747 |
57. | .9317088 .2156759 .3196099 .182546 |
58. | 1.702927 -.3447403 .3353474 .2003875 |
59. | 1.473022 -.2530129 .3326896 .1896515 |
60. | 1.682811 -.2720317 .3705245 .2063351 |
|---------------------------------------------|
61. | .6542128 .2302268 .3491033 .2010015 |
62. | 1.165842 -.0671957 .3879382 .2478161 |
63. | 1.029554 -.1176669 .3528232 .2013738 |
64. | 1.0653 -.0589385 .3494984 .1894278 |
65. | 1.420697 -.2314698 .3520888 .1865833 |
|---------------------------------------------|
66. | .7594075 -.0172845 .3335903 .1903298 |
67. | .8578922 .1619801 .3003038 .1751477 |
68. | .8779113 .0121665 .3831084 .2166708 |
69. | 1.238163 -.0895247 .3514877 .1983345 |
70. | .7981394 .1349785 .3298865 .1955188 |
|---------------------------------------------|
71. | .9727077 -.0817856 .3174344 .1859245 |
72. | 1.302738 -.1700593 .331875 .1818135 |
73. | .5184161 .3513413 .3169683 .1842855 |
74. | 1.125606 -.0406142 .3046075 .1712469 |
75. | .8996528 .1580638 .2550585 .1488854 |
|---------------------------------------------|
76. | 1.125181 -.0344631 .4057042 .2251263 |
77. | 1.081219 .078739 .3174917 .1895205 |
78. | .6915819 .2482137 .359138 .2152488 |
79. | 1.00062 .0523689 .3447164 .1869579 |
80. | .931664 -.0390107 .377801 .2205955 |
|---------------------------------------------|
81. | .8458769 .0982409 .2950777 .1672963 |
82. | 1.580999 -.3732261 .3282151 .1774476 |
83. | .5340353 .2836426 .4034976 .2320504 |
84. | .8332487 -.0222312 .4028482 .2226764 |
85. | 1.344916 -.2727568 .3318824 .1824621 |
|---------------------------------------------|
86. | 1.247872 -.199703 .3826075 .2064648 |
87. | 1.27917 -.1025685 .3417996 .1906614 |
88. | 1.20724 -.0420212 .3270128 .1705868 |
89. | .543729 .1554625 .3901327 .2110453 |
90. | .7865372 .2340246 .3857054 .2325107 |
|---------------------------------------------|
91. | .8083475 .0642658 .3591764 .2137516 |
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797. | 1.436815 -.2803494 .3210194 .1900417 |
798. | 1.156557 -.1356452 .36822 .2061686 |
799. | 1.423557 -.1597084 .3578435 .1942695 |
800. | 1.484803 -.0893092 .3485827 .1914623 |
|---------------------------------------------|
801. | .4961059 .2575926 .3646477 .2252825 |
802. | 1.033517 -.0623536 .3316059 .1860489 |
803. | .7932764 .2570933 .3564243 .2047823 |
804. | 1.160051 -.1885673 .3518971 .204466 |
805. | 1.306173 -.1931553 .3585113 .2052058 |
|---------------------------------------------|
806. | .5950397 .3202751 .3381769 .2029853 |
807. | .9397225 -.0462108 .3880099 .2232885 |
808. | 1.329487 -.3177004 .2999325 .1759211 |
809. | .8062749 .0122603 .3859137 .2401851 |
810. | 1.155069 .1235131 .3595192 .1886082 |
|---------------------------------------------|
811. | .9713209 .0242472 .3520572 .1935347 |
812. | 1.334491 -.215009 .3649377 .2337942 |
813. | .5892357 .0061448 .3468009 .202033 |
814. | .232676 .347793 .354587 .1891508 |
815. | 1.161033 -.0365228 .3918291 .2137925 |
|---------------------------------------------|
816. | 1.00524 -.0109887 .3701323 .2288353 |
817. | 1.151127 .0181592 .317352 .1956102 |
818. | .899319 .0371082 .2930296 .1656442 |
819. | 1.271998 -.1304655 .3483558 .2142567 |
820. | 1.048512 .2248115 .3343898 .1742826 |
|---------------------------------------------|
821. | 1.28824 .0157052 .3349226 .1886152 |
822. | 1.278427 -.1597045 .3640887 .1918621 |
823. | .7423126 .1138327 .4379664 .2441941 |
824. | .6058474 .1486301 .2959269 .1705443 |
825. | .8501415 .0822345 .3344595 .2054213 |
|---------------------------------------------|
826. | 1.019415 -.0624252 .3923177 .2254678 |
827. | 1.155179 -.1507675 .4090551 .2387191 |
828. | 1.242861 -.0801937 .4050829 .2369936 |
829. | 1.27669 -.1163692 .2939452 .1728688 |
830. | .9323171 .1977986 .3394299 .198289 |
|---------------------------------------------|
831. | 1.009066 -.0819697 .3903432 .2156429 |
832. | 1.36975 -.0388307 .3406187 .2043038 |
833. | .9393652 .092525 .3623972 .1950021 |
834. | .8525076 .1368396 .3522632 .2002404 |
835. | .4337983 .2806863 .3987406 .2217025 |
|---------------------------------------------|
836. | 1.160757 -.038684 .3556342 .2131129 |
837. | 1.287951 -.2360395 .3501667 .1998951 |
838. | 1.064209 -.0692198 .331998 .1727498 |
839. | .2489686 .4191914 .2988425 .1734362 |
840. | .7351713 .2622586 .3370603 .1996417 |
|---------------------------------------------|
841. | .4664417 .2433368 .3362674 .1938171 |
842. | .9064234 .0376064 .328966 .1904148 |
843. | 1.115086 .1586838 .3735167 .22004 |
844. | .9772663 .2079671 .4116682 .2474415 |
845. | 1.174296 .0225203 .3277637 .2008734 |
|---------------------------------------------|
846. | 1.043051 -.0771709 .3745323 .219424 |
847. | 1.142819 -.0206943 .3766043 .2067987 |
848. | 1.419234 -.0932343 .3489063 .2022359 |
849. | .8583509 .0872972 .3479745 .1956862 |
850. | 1.11275 -.1219708 .3682424 .2363365 |
|---------------------------------------------|
851. | .2877382 .3326402 .3080251 .1695375 |
852. | .6654427 .1965856 .4060487 .2370801 |
853. | .758756 .173042 .3728602 .2261969 |
854. | .6519112 .138489 .3196657 .18708 |
855. | 1.100505 -.1706354 .3605649 .189296 |
|---------------------------------------------|
856. | .8470733 -.0791004 .3287062 .1924022 |
857. | .9158201 .0454905 .3236552 .1874062 |
858. | 1.291229 -.2033289 .3358461 .2029092 |
859. | .763958 .0154989 .3856918 .2131236 |
860. | 1.41587 -.231079 .3676445 .2017126 |
|---------------------------------------------|
861. | .9897915 .0472741 .3322713 .1933117 |
862. | 1.428848 -.245996 .3781465 .2106395 |
863. | 1.090091 -.0670238 .3187988 .1990434 |
864. | 1.019593 -.0140983 .3922096 .2195377 |
865. | 1.444907 -.2124887 .3538134 .1919906 |
|---------------------------------------------|
866. | 1.292564 -.2250352 .3205111 .1963519 |
867. | .95004 .0723572 .2599099 .1580212 |
868. | 1.688427 -.2470166 .3533699 .2105144 |
869. | 1.451715 -.2483739 .3357756 .1968539 |
870. | 1.440813 -.0374388 .3892751 .2043838 |
|---------------------------------------------|
871. | .5260544 .2232057 .3385885 .1886114 |
872. | .7580446 .3421271 .3337114 .2022372 |
873. | 1.103438 .0811395 .3161386 .180174 |
874. | .7739359 .2444277 .3658357 .2177707 |
875. | .3873697 .4594476 .3680467 .2086359 |
|---------------------------------------------|
876. | .8352167 .0328928 .3370255 .1947017 |
877. | .481302 .410318 .315282 .1845106 |
878. | .78063 .1543005 .39317 .2175139 |
879. | 1.072418 -.1519731 .3205635 .1814721 |
880. | .2822637 .3751816 .361286 .1895247 |
|---------------------------------------------|
881. | .8056785 .1608467 .3641976 .213864 |
882. | .7885307 .2243712 .3240413 .1955443 |
883. | .7170353 .0996573 .3082252 .1726531 |
884. | 1.670215 -.4663383 .3399889 .1949617 |
885. | 1.133049 -.0937843 .3293694 .1873846 |
|---------------------------------------------|
886. | .5249521 .3193793 .3488646 .2154657 |
887. | .7021254 .1465936 .3505778 .2141192 |
888. | 1.401224 -.0606648 .3467372 .1943885 |
889. | 1.552776 -.3648231 .3347985 .2014162 |
890. | .9442059 -.0335972 .3994018 .2262499 |
|---------------------------------------------|
891. | 1.26716 -.0970118 .3495046 .212167 |
892. | 1.010396 -.1001834 .3569418 .2025429 |
893. | .8142053 .0155246 .3331301 .1796457 |
894. | 1.626649 -.0948997 .3326831 .1936528 |
895. | 1.226414 -.2020371 .3719013 .2056536 |
|---------------------------------------------|
896. | 1.342801 -.156519 .3762227 .226755 |
897. | .6142105 .2354795 .3524901 .2028186 |
898. | 1.239767 -.1139446 .3592923 .1998718 |
899. | 2.244132 -.7527432 .3799201 .2397055 |
900. | 1.135859 -.3023118 .3800519 .2238632 |
|---------------------------------------------|
901. | .5014794 .2006493 .3321605 .1899285 |
902. | 1.054357 -.013029 .3198121 .1838669 |
903. | .6458868 .1742113 .3569174 .2042981 |
904. | .8459685 .1921115 .3514601 .2005788 |
905. | .6595617 .3144333 .3284964 .1947318 |
|---------------------------------------------|
906. | 1.330089 -.2676432 .3863392 .2417543 |
907. | 1.079408 .0353684 .3801109 .2063686 |
908. | .3914513 .3297294 .3393564 .2134114 |
909. | 1.317518 -.4244623 .3299989 .1878529 |
910. | .73827 .1585707 .3449445 .2007971 |
|---------------------------------------------|
911. | .3292433 .3861096 .3581176 .2016931 |
912. | 1.000364 .0062573 .3250114 .2017031 |
913. | .5317419 .2360094 .3354806 .1883057 |
914. | 1.356842 -.1112595 .3189693 .1890174 |
915. | 1.532782 -.3113945 .3801174 .2188604 |
|---------------------------------------------|
916. | 1.061119 .0191994 .3151579 .1841163 |
917. | 1.101813 -.0393087 .3067585 .1636194 |
918. | 1.104865 -.0734121 .3815071 .2185131 |
919. | 1.213884 -.2204942 .355473 .192365 |
920. | 1.464735 -.1357801 .3883435 .2329661 |
|---------------------------------------------|
921. | .8883349 .0882232 .393947 .2054442 |
922. | 1.321624 -.2779372 .3527744 .2131498 |
923. | 1.507772 -.2259034 .4046329 .2161744 |
924. | .514246 .4596507 .3631078 .2150726 |
925. | .6914377 .2433898 .3718582 .215287 |
|---------------------------------------------|
926. | .795937 -.0916461 .3305958 .1833088 |
927. | 1.645793 -.3848728 .3220698 .1981398 |
928. | .961314 -.0074425 .3290908 .1813758 |
929. | .7639251 .1140931 .3564588 .1935219 |
930. | .7738088 .1735961 .3055497 .1722554 |
|---------------------------------------------|
931. | .6149359 .2840386 .3964808 .2169808 |
932. | 1.973183 -.6628356 .3615784 .2085836 |
933. | .4994077 .4291864 .3434621 .1957349 |
934. | .4769991 .277117 .3478401 .1924243 |
935. | .6759509 .0790283 .3277582 .1815727 |
|---------------------------------------------|
936. | 1.484794 -.2257848 .3987181 .2349916 |
937. | 1.31455 -.112719 .3223773 .1918533 |
938. | .7774434 .084122 .3751278 .2197167 |
939. | .7525656 .1632763 .3666228 .206468 |
940. | .8254387 .1449177 .3272327 .1841201 |
|---------------------------------------------|
941. | .8694506 .0835562 .3557163 .2153461 |
942. | 1.351655 -.2288294 .3271168 .1909661 |
943. | 1.075369 -.1235456 .3482618 .1947705 |
944. | .3949203 .4405447 .3432383 .2028679 |
945. | .9644006 .1667094 .3485558 .1843775 |
|---------------------------------------------|
946. | 1.039182 .1222938 .3367328 .2022986 |
947. | 1.774318 -.477198 .3201205 .1855502 |
948. | 1.532695 -.3986749 .3253837 .1886529 |
949. | .5012421 .1367282 .3272968 .200414 |
950. | .7757585 .0655139 .3456603 .193249 |
|---------------------------------------------|
951. | 1.227082 -.1729744 .3303602 .1891309 |
952. | .8152466 .233351 .3565302 .2093295 |
953. | .7737159 .1450677 .3125713 .182259 |
954. | .5713038 .1856057 .3460566 .2109566 |
955. | .8085284 .1020168 .3171215 .1762995 |
|---------------------------------------------|
956. | .9594128 -.0731273 .3709191 .210137 |
957. | 1.445426 -.0956053 .3149655 .1728466 |
958. | 1.349565 -.2290939 .3442764 .192963 |
959. | 1.06743 -.0704198 .3738375 .2224746 |
960. | 1.428437 -.3059278 .3685649 .2106346 |
|---------------------------------------------|
961. | .8819759 .1359055 .33126 .1937269 |
962. | 1.142837 .0121794 .3816003 .2244789 |
963. | .679534 .3777817 .3447722 .2004587 |
964. | .9606586 -.0586312 .324406 .1852736 |
965. | .4246399 .0851688 .3500434 .2003739 |
|---------------------------------------------|
966. | .837464 -.072106 .308392 .1640315 |
967. | .8452932 .2129013 .3304544 .203481 |
968. | 1.097202 -.0938646 .3142847 .1808106 |
969. | .7280346 .1158969 .3715099 .2156086 |
970. | .7615895 .0654105 .332293 .2022823 |
|---------------------------------------------|
971. | .511885 .219877 .3682644 .2224774 |
972. | .8292047 .1349411 .3283213 .1875177 |
973. | .9007233 -.0231933 .3597834 .2168201 |
974. | .6704969 .1847326 .3855911 .2164368 |
975. | .5344537 .0939824 .3264777 .1912659 |
|---------------------------------------------|
976. | .7335682 .1253774 .319298 .191587 |
977. | 1.380163 -.101974 .3697542 .2154799 |
978. | .8961666 .0104728 .3166674 .1845269 |
979. | .7567766 .069349 .3390926 .1993854 |
980. | .650882 .0974124 .3014756 .169952 |
|---------------------------------------------|
981. | 1.145099 .1567774 .3547437 .1941825 |
982. | .6129782 .152214 .3916048 .2074048 |
983. | 1.682793 -.4439102 .3455535 .2093617 |
984. | .8926481 .0666601 .3505219 .2038047 |
985. | 1.306729 -.1827545 .2646281 .1454333 |
|---------------------------------------------|
986. | 1.185633 -.0167782 .347074 .201593 |
987. | .797076 .1297591 .3606455 .2053377 |
988. | 1.822169 -.4145564 .3475154 .2125618 |
989. | 1.189708 -.0264855 .2991623 .171385 |
990. | 1.023727 -.0348524 .3539976 .1832881 |
|---------------------------------------------|
991. | 1.286882 -.1168466 .3945922 .2130531 |
992. | 1.291631 -.1629395 .2745742 .1590537 |
993. | 1.13847 .0294557 .2964553 .1708743 |
994. | .5840677 .0967491 .3370716 .1923484 |
995. | .9280443 -.1198396 .4305107 .2520569 |
|---------------------------------------------|
996. | 1.172495 -.0798176 .3539177 .2037075 |
997. | 1.009831 .0427201 .3589584 .2171218 |
998. | 1.016992 .034016 .4152612 .2447845 |
999. | 1.367975 -.20746 .3756332 .2243822 |
1000. | .8791329 -.1972916 .3381031 .174095 |
+---------------------------------------------+
. sum
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
b_x1 | 1000 1.013201 .3503711 -.0500266 2.274912
b_cons | 1000 -.0008153 .2060673 -.7527432 .6365386
se_x1 | 1000 .3479545 .0299893 .2533616 .4525876
se_cons | 1000 .2002391 .0185406 .1440955 .267114
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. simulate mc _b _se, reps(10000)
command: mc
statistics: b_x1 = _b[x1]
b_cons = _b[_cons]
se_x1 = _se[x1]
se_cons = _se[_cons]
.
end of do-file
. sum
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
b_x1 | 10000 .9970519 .3513966 -.3363353 2.321597
b_cons | 10000 .0015121 .2038508 -.744823 .7830855
se_x1 | 10000 .3480467 .0298428 .2422152 .4753152
se_cons | 10000 .2007391 .0184818 .13992 .2934018
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. simulate mc _b _se, reps(10000)
command: mc
statistics: b_x1 = _b[x1]
b_cons = _b[_cons]
se_x1 = _se[x1]
se_cons = _se[_cons]
--Break--
r(1);
end of do-file
--Break--
r(1);
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. simulate mc _b _se, reps(1000)
command: mc
statistics: b_x1 = _b[x1]
b_cons = _b[_cons]
se_x1 = _se[x1]
se_cons = _se[_cons]
.
end of do-file
. kdensity b_x1
. do "C:\DOCUME~1\labuser\LOCALS~1\Temp\STD03000000.tmp"
. clear all
.
. capture program drop mc
.
. program mc
1. drop _all
2.
. set obs 10000
3.
. gen x1 = runiform()
4. gen x2 = runiform()
5. gen u = 1*rnormal()
6.
. gen y = 0 + 1*x1 /* + -1*x2 */ + u
7.
. regress y x1
8. end
.
. mc
obs was 0, now 10000
Source | SS df MS Number of obs = 10000
-------------+------------------------------ F( 1, 9998) = 809.67
Model | 802.066657 1 802.066657 Prob > F = 0.0000
Residual | 9904.16227 9998 .99061435 R-squared = 0.0749
-------------+------------------------------ Adj R-squared = 0.0748
Total | 10706.2289 9999 1.07072997 Root MSE = .9953
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .9793364 .0344175 28.45 0.000 .9118712 1.046802
_cons | .0235576 .0199703 1.18 0.238 -.0155881 .0627034
------------------------------------------------------------------------------
.
end of do-file
.
