蒙特卡罗模拟
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Notes:
1. Unicode is supported; see help unicode_advice.
2. More than 2 billion observations are allowed; see help obs_advice.
3. Maximum number of variables is set to 5000; see help set_maxvar.
4. New update available; type -update all-
running c:\ado\plus\profile.do ...
. use "D:\A-Education\Study\计量经济学\data\dta\grilic.dta", clear
. regress lnw s expr tenure smsa rns
Source | SS df MS Number of obs = 758
-------------+---------------------------------- F(5, 752) = 81.75
Model | 49.0478814 5 9.80957628 Prob > F = 0.0000
Residual | 90.2382684 752 .119997697 R-squared = 0.3521
-------------+---------------------------------- Adj R-squared = 0.3478
Total | 139.28615 757 .183997556 Root MSE = .34641
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .102643 .0058488 17.55 0.000 .0911611 .114125
expr | .0381189 .0063268 6.02 0.000 .0256986 .0505392
tenure | .0356146 .0077424 4.60 0.000 .0204153 .0508138
smsa | .1396666 .0280821 4.97 0.000 .0845379 .1947954
rns | -.0840797 .0287973 -2.92 0.004 -.1406124 -.0275471
_cons | 4.103675 .085097 48.22 0.000 3.936619 4.270731
------------------------------------------------------------------------------
. vce
Covariance matrix of coefficients of regress model
e(V) | s expr tenure smsa rns _cons
-------------+------------------------------------------------------------------------
s | .00003421
expr | 8.660e-06 .00004003
tenure | -3.997e-08 -.00001107 .00005994
smsa | -.0000144 3.261e-06 -7.819e-06 .00078861
rns | 8.524e-06 7.334e-07 7.259e-06 .00012486 .00082928
_cons | -.00046567 -.00016778 -.00008646 -.00038746 -.00043997 .0072415
. regress lnw s expr tenure smsa rns,noc
Source | SS df MS Number of obs = 758
-------------+---------------------------------- F(5, 753) = 9902.73
Model | 24282.9531 5 4856.59061 Prob > F = 0.0000
Residual | 369.293555 753 .490429688 R-squared = 0.9850
-------------+---------------------------------- Adj R-squared = 0.9849
Total | 24652.2466 758 32.5227528 Root MSE = .70031
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .3665333 .0041742 87.81 0.000 .3583389 .3747277
expr | .1331991 .0121535 10.96 0.000 .1093403 .1570578
tenure | .0846129 .0155168 5.45 0.000 .0541515 .1150743
smsa | .3592339 .0560206 6.41 0.000 .2492588 .4692089
rns | .1652489 .0572715 2.89 0.004 .0528181 .2776796
------------------------------------------------------------------------------
. regress lnw s expr tenure smsa rns if rns
note: rns omitted because of collinearity
Source | SS df MS Number of obs = 204
-------------+---------------------------------- F(4, 199) = 36.07
Model | 17.603542 4 4.40088551 Prob > F = 0.0000
Residual | 24.2783596 199 .122001807 R-squared = 0.4203
-------------+---------------------------------- Adj R-squared = 0.4087
Total | 41.8819016 203 .206314786 Root MSE = .34929
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .1198242 .0113156 10.59 0.000 .0975103 .1421381
expr | .0451903 .0122572 3.69 0.000 .0210197 .069361
tenure | .0092643 .0156779 0.59 0.555 -.0216518 .0401804
smsa | .1746563 .0506762 3.45 0.001 .0747251 .2745876
rns | 0 (omitted)
_cons | 3.806148 .1586202 24.00 0.000 3.493356 4.11894
------------------------------------------------------------------------------
. regress lnw s expr tenure smsa rns if ~rns
note: rns omitted because of collinearity
Source | SS df MS Number of obs = 554
-------------+---------------------------------- F(4, 549) = 62.45
Model | 29.486457 4 7.37161426 Prob > F = 0.0000
Residual | 64.8019636 549 .118036364 R-squared = 0.3127
-------------+---------------------------------- Adj R-squared = 0.3077
Total | 94.2884207 553 .170503473 Root MSE = .34356
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .0944787 .0068365 13.82 0.000 .0810498 .1079076
expr | .0358675 .0073558 4.88 0.000 .0214184 .0503165
tenure | .0455117 .0088792 5.13 0.000 .0280703 .0629531
smsa | .1199364 .0337443 3.55 0.000 .0536526 .1862202
rns | 0 (omitted)
_cons | 4.214014 .0995796 42.32 0.000 4.018411 4.409618
------------------------------------------------------------------------------
. regress lnw s expr tenure smsa rns if s>=12
Source | SS df MS Number of obs = 679
-------------+---------------------------------- F(5, 673) = 69.81
Model | 41.8750434 5 8.37500867 Prob > F = 0.0000
Residual | 80.7410668 673 .119971867 R-squared = 0.3415
-------------+---------------------------------- Adj R-squared = 0.3366
Total | 122.61611 678 .18084972 Root MSE = .34637
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .1077261 .0066792 16.13 0.000 .0946115 .1208408
expr | .0344524 .0071189 4.84 0.000 .0204745 .0484304
tenure | .0363033 .0082594 4.40 0.000 .0200859 .0525206
smsa | .1583146 .0298248 5.31 0.000 .0997537 .2168754
rns | -.074063 .0308884 -2.40 0.017 -.1347123 -.0134137
_cons | 4.015335 .098159 40.91 0.000 3.8226 4.20807
------------------------------------------------------------------------------
. regress lnw s expr tenure smsa rns if s>=12 & rns
note: rns omitted because of collinearity
Source | SS df MS Number of obs = 174
-------------+---------------------------------- F(4, 169) = 32.17
Model | 15.404067 4 3.85101675 Prob > F = 0.0000
Residual | 20.2300414 169 .119704387 R-squared = 0.4323
-------------+---------------------------------- Adj R-squared = 0.4188
Total | 35.6341084 173 .205977505 Root MSE = .34598
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .1269124 .0131847 9.63 0.000 .1008845 .1529404
expr | .0226531 .0156062 1.45 0.148 -.0081551 .0534613
tenure | .0146869 .0182079 0.81 0.421 -.0212573 .0506312
smsa | .2136309 .0548788 3.89 0.000 .1052947 .3219671
rns | 0 (omitted)
_cons | 3.699026 .1873691 19.74 0.000 3.329141 4.068912
------------------------------------------------------------------------------
. quietly regress lnw s expr tenure smsa rns,noc
. quietly regress lnw s expr tenure smsa rns
. predict lnw_pre
(option xb assumed; fitted values)
. predict e,residual
. save "D:\A-Education\Study\计量经济学\data\practice\grilic_0103.dta"
file D:\A-Education\Study\计量经济学\data\practice\grilic_0103.dta saved
. use "D:\A-Education\Study\计量经济学\data\practice\蒙特卡罗模拟0102.dta", clear
. do "D:\A-Education\Study\计量经济学\data\practice\蒙特卡罗模拟0102.do"
. use "D:\A-Education\Study\计量经济学\data\dta\grilic_small.dta", clear
. use "D:\A-Education\Study\计量经济学\data\dta\grilic.dta", clear
. regress lnw s
Source | SS df MS Number of obs = 758
-------------+---------------------------------- F(1, 756) = 255.70
Model | 35.2039946 1 35.2039946 Prob > F = 0.0000
Residual | 104.082155 756 .137674809 R-squared = 0.2527
-------------+---------------------------------- Adj R-squared = 0.2518
Total | 139.28615 757 .183997556 Root MSE = .37105
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .0966245 .0060425 15.99 0.000 .0847624 .1084866
_cons | 4.391486 .0821136 53.48 0.000 4.230288 4.552684
------------------------------------------------------------------------------
. regress lnw s,noc
Source | SS df MS Number of obs = 758
-------------+---------------------------------- F(1, 757) = 36727.24
Model | 24154.3906 1 24154.3906 Prob > F = 0.0000
Residual | 497.855977 757 .657669719 R-squared = 0.9798
-------------+---------------------------------- Adj R-squared = 0.9798
Total | 24652.2466 758 32.5227528 Root MSE = .81097
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .4154001 .0021676 191.64 0.000 .4111449 .4196553
------------------------------------------------------------------------------
. sum s
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
s | 758 13.40501 2.231828 9 18
. return list
scalars:
r(N) = 758
r(sum_w) = 758
r(mean) = 13.40501319261214
r(Var) = 4.981058057949899
r(sd) = 2.231828411403955
r(min) = 9
r(max) = 18
r(sum) = 10161
. display r(sd)/r(mean)
.16649207
. reg lnw s
Source | SS df MS Number of obs = 758
-------------+---------------------------------- F(1, 756) = 255.70
Model | 35.2039946 1 35.2039946 Prob > F = 0.0000
Residual | 104.082155 756 .137674809 R-squared = 0.2527
-------------+---------------------------------- Adj R-squared = 0.2518
Total | 139.28615 757 .183997556 Root MSE = .37105
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .0966245 .0060425 15.99 0.000 .0847624 .1084866
_cons | 4.391486 .0821136 53.48 0.000 4.230288 4.552684
------------------------------------------------------------------------------
. ereturn list
scalars:
e(N) = 758
e(df_m) = 1
e(df_r) = 756
e(F) = 255.7039662336325
e(r2) = .2527458374860051
e(rmse) = .3710455612613365
e(mss) = 35.20399459202193
e(rss) = 104.0821552499956
e(r2_a) = .2517574060541082
e(ll) = -323.0498302841153
e(ll_0) = -433.4714451849196
e(rank) = 2
macros:
e(cmdline) : "regress lnw s"
e(title) : "Linear regression"
e(marginsok) : "XB default"
e(vce) : "ols"
e(depvar) : "lnw"
e(cmd) : "regress"
e(properties) : "b V"
e(predict) : "regres_p"
e(model) : "ols"
e(estat_cmd) : "regress_estat"
matrices:
e(b) : 1 x 2
e(V) : 2 x 2
functions:
e(sample)
. clear
. set obs 30
number of observations (_N) was 0, now 30
. set seed 10101
. gen x = rnormal(3,4)
. gen e = rnormal(0,9)
. gen y = 1 + 2*x + e
. reg y x
Source | SS df MS Number of obs = 30
-------------+---------------------------------- F(1, 28) = 37.50
Model | 2832.54477 1 2832.54477 Prob > F = 0.0000
Residual | 2114.75553 28 75.5269833 R-squared = 0.5725
-------------+---------------------------------- Adj R-squared = 0.5573
Total | 4947.3003 29 170.596562 Root MSE = 8.6906
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | 2.01285 .3286805 6.12 0.000 1.339578 2.686121
_cons | 1.042369 1.90375 0.55 0.588 -2.857286 4.942023
------------------------------------------------------------------------------
. twoway function PRF=1+2*x,range(-5 15) || scatter y x || lfit y x,lpattern(dash)
. save "D:\A-Education\Study\计量经济学\data\dta\蒙特卡罗模拟0102.dta"
运行结果.png
Stata命令分类
- r命令：返回非估计结果的scalar变量
- e命令：返回估计问题的scalar变量结果
//返回r命令的结果
return list
//返回e命令的结果
ereturn list
. ereturn list
scalars:
e(rank) = 2
e(ll_0) = -119.1491549584417
e(ll) = -106.400612732956
e(r2_a) = .5572772251452395
e(rss) = 2114.755532755231
e(mss) = 2832.544765465164
e(rmse) = 8.690626174947742
e(r2) = .5725435277264381
e(F) = 37.50374556518744
e(df_r) = 28
e(df_m) = 1
e(N) = 30
macros:
e(cmdline) : "regress y x"
e(title) : "Linear regression"
e(marginsok) : "XB default"
e(vce) : "ols"
e(depvar) : "y"
e(cmd) : "regress"
e(properties) : "b V"
e(predict) : "regres_p"
e(model) : "ols"
e(estat_cmd) : "regress_estat"
matrices:
e(b) : 1 x 2
e(V) : 2 x 2
functions:
e(sample)
# 多元回归
regress y x1 x2
# 自动预测lny的值
predict pre_lny
# 自动计算残差项e
predict e,residual
# 展示真实值，预测值和残差
list lny pre_lny e
# 区分真实值和拟合值-图示法
line lny pre_lny year,lpattern(solid dashed)
# 有常数项的
regress y x
# 无常数项的
regress y x, noconstant
regress y x, noc
regression.png
stata其他命令.png
# rules
regress y x1 x2 x3
regress lnw s expr tenure smsa rns
Source | SS df MS Number of obs = 758
-------------+---------------------------------- F(5, 752) = 81.75
Model | 49.0478814 5 9.80957628 Prob > F = 0.0000
Residual | 90.2382684 752 .119997697 R-squared = 0.3521
-------------+---------------------------------- Adj R-squared = 0.3478
Total | 139.28615 757 .183997556 Root MSE = .34641
vce
# 显示所有洗漱的协方差矩阵
Covariance matrix of coefficients of regress model
e(V) | s expr tenure smsa rns _cons
-------------+------------------------------------------------------------------------
s | .00003421
expr | 8.660e-06 .00004003
tenure | -3.997e-08 -.00001107 .00005994
smsa | -.0000144 3.261e-06 -7.819e-06 .00078861
rns | 8.524e-06 7.334e-07 7.259e-06 .00012486 .00082928
_cons | -.00046567 -.00016778 -.00008646 -.00038746 -.00043997 .0072415
# 无常数项回归
regress lnw s expr tenure smsa rns,noc
Source | SS df MS Number of obs = 758
-------------+---------------------------------- F(5, 753) = 9902.73
Model | 24282.9531 5 4856.59061 Prob > F = 0.0000
Residual | 369.293555 753 .490429688 R-squared = 0.9850
-------------+---------------------------------- Adj R-squared = 0.9849
Total | 24652.2466 758 32.5227528 Root MSE = .70031
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .3665333 .0041742 87.81 0.000 .3583389 .3747277
expr | .1331991 .0121535 10.96 0.000 .1093403 .1570578
tenure | .0846129 .0155168 5.45 0.000 .0541515 .1150743
smsa | .3592339 .0560206 6.41 0.000 .2492588 .4692089
rns | .1652489 .0572715 2.89 0.004 .0528181 .2776796
------------------------------------------------------------------------------
# 南方居民
regress lnw s expr tenure smsa rns if rns
note: rns omitted because of collinearity
Source | SS df MS Number of obs = 204
-------------+---------------------------------- F(4, 199) = 36.07
Model | 17.603542 4 4.40088551 Prob > F = 0.0000
Residual | 24.2783596 199 .122001807 R-squared = 0.4203
-------------+---------------------------------- Adj R-squared = 0.4087
Total | 41.8819016 203 .206314786 Root MSE = .34929
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .1198242 .0113156 10.59 0.000 .0975103 .1421381
expr | .0451903 .0122572 3.69 0.000 .0210197 .069361
tenure | .0092643 .0156779 0.59 0.555 -.0216518 .0401804
smsa | .1746563 .0506762 3.45 0.001 .0747251 .2745876
rns | 0 (omitted)
_cons | 3.806148 .1586202 24.00 0.000 3.493356 4.11894
------------------------------------------------------------------------------
# 非南方居民
regress lnw s expr tenure smsa rns if ~rns
note: rns omitted because of collinearity
Source | SS df MS Number of obs = 554
-------------+---------------------------------- F(4, 549) = 62.45
Model | 29.486457 4 7.37161426 Prob > F = 0.0000
Residual | 64.8019636 549 .118036364 R-squared = 0.3127
-------------+---------------------------------- Adj R-squared = 0.3077
Total | 94.2884207 553 .170503473 Root MSE = .34356
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .0944787 .0068365 13.82 0.000 .0810498 .1079076
expr | .0358675 .0073558 4.88 0.000 .0214184 .0503165
tenure | .0455117 .0088792 5.13 0.000 .0280703 .0629531
smsa | .1199364 .0337443 3.55 0.000 .0536526 .1862202
rns | 0 (omitted)
_cons | 4.214014 .0995796 42.32 0.000 4.018411 4.409618
------------------------------------------------------------------------------
# 教育年限>=12年
regress lnw s expr tenure smsa rns if s>=12
Source | SS df MS Number of obs = 679
-------------+---------------------------------- F(5, 673) = 69.81
Model | 41.8750434 5 8.37500867 Prob > F = 0.0000
Residual | 80.7410668 673 .119971867 R-squared = 0.3415
-------------+---------------------------------- Adj R-squared = 0.3366
Total | 122.61611 678 .18084972 Root MSE = .34637
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .1077261 .0066792 16.13 0.000 .0946115 .1208408
expr | .0344524 .0071189 4.84 0.000 .0204745 .0484304
tenure | .0363033 .0082594 4.40 0.000 .0200859 .0525206
smsa | .1583146 .0298248 5.31 0.000 .0997537 .2168754
rns | -.074063 .0308884 -2.40 0.017 -.1347123 -.0134137
_cons | 4.015335 .098159 40.91 0.000 3.8226 4.20807
------------------------------------------------------------------------------
# 教育年限大于等于12的南方居民
regress lnw s expr tenure smsa rns if s>=12 & rns
note: rns omitted because of collinearity
Source | SS df MS Number of obs = 174
-------------+---------------------------------- F(4, 169) = 32.17
Model | 15.404067 4 3.85101675 Prob > F = 0.0000
Residual | 20.2300414 169 .119704387 R-squared = 0.4323
-------------+---------------------------------- Adj R-squared = 0.4188
Total | 35.6341084 173 .205977505 Root MSE = .34598
------------------------------------------------------------------------------
lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
s | .1269124 .0131847 9.63 0.000 .1008845 .1529404
expr | .0226531 .0156062 1.45 0.148 -.0081551 .0534613
tenure | .0146869 .0182079 0.81 0.421 -.0212573 .0506312
smsa | .2136309 .0548788 3.89 0.000 .1052947 .3219671
rns | 0 (omitted)
_cons | 3.699026 .1873691 19.74 0.000 3.329141 4.068912
------------------------------------------------------------------------------
# 不显示结果
quietly regress lnw s expr tenure smsa rns,noc
1.png
2.png
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9.png
今天计量经济学学到第六章了
好样的，准备晚上再学到一点钟就睡觉，加油加油~
老师给我发论文了.png
老师今天给我发论文了
我慌死了，打开PDF傻眼了，真的好多哇……
全英文论文，加起来四篇应该差不多200页
不能抱怨！
想一想如何能够尽量少走弯路读完
寒假可能很难回家了
准备在家过个大年就回武汉
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