推荐2篇《数量经济技术经济研究》上含Bartik 工具变量法论文（附代码复现)
论文1、数字化进程与线上市场配置效率——基于平台流量倾斜的微观证据
《数量经济技术经济研究》2023年第6期主要计量方法命集合（合成DID、强度DID、断点等） (qq.com)里面提到《数字化进程与线上市场配置效率——基于平台流量倾斜的微观证据》这篇文章主要使用到了如下命令：
结果输出：logout+esttab
xtreg、regife、areg、xtbalance
培根分解：培根分解
而该文里面就使用到了Bartik 工具变量法，对应命令为ivreghdfe，选取的第一个工具变量是不含酒店自身的各城市特牌酒店占比的均值（digital_city），在这个工具变量基础上进一步构造 Bartik 工具变量（digital_bartik），用 digital_city 初始值乘以每日全国 酒店中特牌酒店占比的变化程度来表示，第三个工具变量是各城市的邮局数量（post）。
代码命令为：
ivreghdfe occupancy (digital=digital_city) $cx i.hotel i.date, absorb(i.city#i.date) cluster(city)
est store m1
ivreghdfe occupancy (digital=digital_bartik) $cx i.hotel i.date, absorb(i.city#i.date) cluster(city)
est store m2
ivreghdfe occupancy (digital=post) $cx i.hotel i.date, absorb(i.city#i.date) cluster(city)
est store m3
ivreghdfe occupancy (digital=digital_city digital_bartik post) $cx i.hotel i.date, absorb(i.city#i.date) cluster(city)
est store m4
logout, save(mylogout2) word replace: ///
esttab m1 m2 m3 m4, star(* 0.1 ** 0.05 *** 0.01) ///
b(%6.3f) se(%6.3f) compress nogap drop(_I* o._* *.date) stats(Cluster N r2_a)
2：经济集聚对管理者薪酬的影响及机制研究
2023年第3期《数量经济技术经济研究》目录及计量方法汇总表（DID、DDD等） 里面提到，《经济集聚对管理者薪酬的影响及机制研究》这篇文章里面用到了Bartik 工具变量法，详见：
相关结果为：
下面使用Stata软件进行操作，结果为：
*增速，单变量回归，有固定效应
. ivreg2 Y1 ( X1 = IV ) i.year i.Industry ,r
IV (2SLS) estimation
--------------------
Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity
Number of obs = 17456
F( 56, 17399) = 2.38
Prob > F = 0.0000
Total (centered) SS = 18776.43889 Centered R2 = 0.0102
Total (uncentered) SS = 19992.20224 Uncentered R2 = 0.0704
Residual SS = 18585.19477 Root MSE = 1.032
------------------------------------------------------------------------------
| Robust
Y1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X1 | .6725086 .212346 3.17 0.002 .256318 1.088699
|
year |
2010 | .1215809 .0298943 4.07 0.000 .0629891 .1801728
2011 | .0247482 .0273428 0.91 0.365 -.0288428 .0783391
2012 | .0014736 .0297414 0.05 0.960 -.0568184 .0597657
2013 | .0268862 .0318791 0.84 0.399 -.0355958 .0893681
2014 | .021012 .031433 0.67 0.504 -.0405955 .0826196
2015 | .1670464 .0405426 4.12 0.000 .0875844 .2465085
2016 | .0654678 .0336572 1.95 0.052 -.0004992 .1314347
2017 | .0865667 .0301613 2.87 0.004 .0274516 .1456818
2018 | .0524127 .0296073 1.77 0.077 -.0056165 .110442
2019 | .0727127 .0294299 2.47 0.013 .0150311 .1303943
|
Industry |
2 | .0125054 .0605393 0.21 0.836 -.1061494 .1311603
4 | -.0539559 .052427 -1.03 0.303 -.1567109 .0487992
5 | -.0091227 .058876 -0.15 0.877 -.1245176 .1062722
6 | .0089355 .0539559 0.17 0.868 -.096816 .1146871
7 | -.0573878 .0534388 -1.07 0.283 -.1621259 .0473503
8 | -.0396973 .0790597 -0.50 0.616 -.1946515 .115257
9 | .1754699 .0705695 2.49 0.013 .0371562 .3137835
11 | .011996 .0530465 0.23 0.821 -.0919733 .1159653
12 | .0428961 .0790939 0.54 0.588 -.1121251 .1979173
13 | -.0607549 .0959165 -0.63 0.526 -.2487478 .127238
14 | .1574894 .1093813 1.44 0.150 -.056894 .3718729
15 | -.1105923 .0987215 -1.12 0.263 -.3040829 .0828983
16 | .2603571 .3673752 0.71 0.479 -.459685 .9803993
17 | .1626157 .1344223 1.21 0.226 -.1008472 .4260786
18 | -.0286759 .0794236 -0.36 0.718 -.1843433 .1269916
19 | -.040706 .0663305 -0.61 0.539 -.1707114 .0892995
313 | .1118545 .0849387 1.32 0.188 -.0546224 .2783314
314 | .1014908 .0913997 1.11 0.267 -.0776493 .280631
315 | .0041538 .0664088 0.06 0.950 -.1260051 .1343128
317 | .0784468 .0794775 0.99 0.324 -.0773262 .2342198
318 | .0434977 .0871051 0.50 0.618 -.1272251 .2142205
319 | -.0285495 .1123599 -0.25 0.799 -.2487708 .1916717
320 | .3783992 .2608969 1.45 0.147 -.1329492 .8897477
321 | .5293559 .2492355 2.12 0.034 .0408632 1.017849
322 | .1406209 .0994893 1.41 0.158 -.0543745 .3356164
323 | .5128822 .256287 2.00 0.045 .0105689 1.015195
324 | .0192642 .1568947 0.12 0.902 -.2882437 .3267722
325 | .1921242 .1109015 1.73 0.083 -.0252388 .4094873
326 | .067002 .0560329 1.20 0.232 -.0428204 .1768245
327 | .1453782 .061058 2.38 0.017 .0257067 .2650498
328 | .1837576 .1214622 1.51 0.130 -.0543039 .4218191
329 | .0930746 .0752854 1.24 0.216 -.0544821 .2406313
330 | .1009524 .0688623 1.47 0.143 -.0340152 .23592
331 | .0272065 .065406 0.42 0.677 -.1009868 .1553999
332 | .1012703 .0759436 1.33 0.182 -.0475764 .250117
333 | .1472191 .0888603 1.66 0.098 -.0269439 .321382
334 | -.0295451 .0567058 -0.52 0.602 -.1406865 .0815963
335 | .0763704 .0624297 1.22 0.221 -.0459895 .1987304
336 | -.0035003 .0581825 -0.06 0.952 -.117536 .1105354
337 | .0028065 .0684146 0.04 0.967 -.1312837 .1368966
338 | .1241643 .0627826 1.98 0.048 .0011127 .247216
339 | .1042159 .056523 1.84 0.065 -.0065672 .2149989
340 | -.0133766 .0881525 -0.15 0.879 -.1861523 .1593991
341 | .0408389 .0957528 0.43 0.670 -.1468331 .228511
342 | -.0750502 .1915176 -0.39 0.695 -.4504179 .3003174
|
_cons | .1097764 .0513252 2.14 0.032 .0091809 .2103719
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic): 3056.922
Chi-sq(1) P-val = 0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic): 4.1e+04
(Kleibergen-Paap rk Wald F statistic): 1.2e+04
Stock-Yogo weak ID test critical values: 10% maximal IV size 16.38
15% maximal IV size 8.96
20% maximal IV size 6.66
25% maximal IV size 5.53
Source: Stock-Yogo (2005). Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments): 0.000
(equation exactly identified)
------------------------------------------------------------------------------
Instrumented: X1
Included instruments: 2010.year 2011.year 2012.year 2013.year 2014.year
2015.year 2016.year 2017.year 2018.year 2019.year
2.Industry 4.Industry 5.Industry 6.Industry 7.Industry
8.Industry 9.Industry 11.Industry 12.Industry 13.Industry
14.Industry 15.Industry 16.Industry 17.Industry
18.Industry 19.Industry 313.Industry 314.Industry
315.Industry 317.Industry 318.Industry 319.Industry
320.Industry 321.Industry 322.Industry 323.Industry
324.Industry 325.Industry 326.Industry 327.Industry
328.Industry 329.Industry 330.Industry 331.Industry
332.Industry 333.Industry 334.Industry 335.Industry
336.Industry 337.Industry 338.Industry 339.Industry
340.Industry 341.Industry 342.Industry
Excluded instruments: IV
------------------------------------------------------------------------------
. est store m1
.
end of do-file
. do "C:\Users\Metrics\AppData\Local\Temp\STDd94_000000.tmp"
. *增速，有控制变量，有固定效应
. ivreg2 Y1 $CV1 ( X1 = IV ) i.year i.Industry ,r
IV (2SLS) estimation
--------------------
Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity
Number of obs = 17456
F( 70, 17385) = 5.13
Prob > F = 0.0000
Total (centered) SS = 18776.43889 Centered R2 = 0.0314
Total (uncentered) SS = 19992.20224 Uncentered R2 = 0.0903
Residual SS = 18186.62419 Root MSE = 1.021
------------------------------------------------------------------------------
| Robust
Y1 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X1 | .6170367 .2261933 2.73 0.006 .173706 1.060367
C1 | .4410059 .050592 8.72 0.000 .3418474 .5401644
C2 | 1.563778 .2001686 7.81 0.000 1.171455 1.956101
C3 | .5379895 .1397377 3.85 0.000 .2641086 .8118704
C4 | -.0535267 .0188274 -2.84 0.004 -.0904277 -.0166257
C9 | .8641802 .6437339 1.34 0.179 -.3975151 2.125875
C10 | .0053687 .0076242 0.70 0.481 -.0095744 .0203118
C11 | .0017653 .0014389 1.23 0.220 -.0010549 .0045854
C12 | -.1519457 .0177583 -8.56 0.000 -.1867512 -.1171401
C13 | .0012924 .0047378 0.27 0.785 -.0079935 .0105783
C14 | .054032 .1541004 0.35 0.726 -.2479992 .3560632
C15 | .0009454 .0230391 0.04 0.967 -.0442104 .0461013
C1601 | -.0055113 .0019027 -2.90 0.004 -.0092405 -.0017822
C17 | -.0104114 .0110692 -0.94 0.347 -.0321066 .0112839
C18 | .0036851 .0064065 0.58 0.565 -.0088714 .0162415
|
year |
2010 | .1211879 .0303007 4.00 0.000 .0617997 .1805761
2011 | .0456823 .0277976 1.64 0.100 -.0088 .1001647
2012 | .033123 .0304946 1.09 0.277 -.0266454 .0928914
2013 | .0478573 .0320974 1.49 0.136 -.0150525 .1107671
2014 | .0348515 .0319909 1.09 0.276 -.0278495 .0975526
2015 | .1557936 .0414453 3.76 0.000 .0745623 .2370248
2016 | .0569625 .0348233 1.64 0.102 -.01129 .125215
2017 | .1000404 .0334688 2.99 0.003 .0344427 .165638
2018 | .0908967 .0340145 2.67 0.008 .0242295 .1575639
2019 | .1069847 .0378384 2.83 0.005 .0328227 .1811467
|
Industry |
2 | .0710487 .0601274 1.18 0.237 -.0467988 .1888963
4 | .0131615 .0523102 0.25 0.801 -.0893646 .1156876
5 | -.0120179 .0588426 -0.20 0.838 -.1273473 .1033115
6 | .0269562 .0534309 0.50 0.614 -.0777665 .1316788
7 | .0282931 .0532636 0.53 0.595 -.0761016 .1326879
8 | .0204934 .0789159 0.26 0.795 -.1341789 .1751657
9 | .1470831 .0698988 2.10 0.035 .010084 .2840822
11 | -.0019466 .0528556 -0.04 0.971 -.1055416 .1016484
12 | .0416995 .0783237 0.53 0.594 -.1118121 .1952111
13 | -.0620064 .0957479 -0.65 0.517 -.2496689 .1256561
14 | .1532148 .1074007 1.43 0.154 -.0572867 .3637163
15 | -.1076664 .1055606 -1.02 0.308 -.3145614 .0992286
16 | .2522736 .3488819 0.72 0.470 -.4315223 .9360695
17 | .0353926 .1454049 0.24 0.808 -.2495957 .3203808
18 | .01166 .0793264 0.15 0.883 -.1438168 .1671368
19 | -.0198063 .0659464 -0.30 0.764 -.1490589 .1094463
313 | .067221 .0828063 0.81 0.417 -.0950764 .2295183
314 | .1048965 .0913048 1.15 0.251 -.0740577 .2838506
315 | .0468403 .0661105 0.71 0.479 -.0827338 .1764144
317 | .0774775 .078528 0.99 0.324 -.0764347 .2313896
318 | .0030159 .0860991 0.04 0.972 -.1657354 .1717671
319 | -.0652098 .1114831 -0.58 0.559 -.2837126 .153293
320 | .3267702 .2576298 1.27 0.205 -.178175 .8317154
321 | .4320583 .2456018 1.76 0.079 -.0493124 .913429
322 | .1569924 .0989472 1.59 0.113 -.0369406 .3509254
323 | .4517886 .2549762 1.77 0.076 -.0479556 .9515329
324 | -.0412193 .1577759 -0.26 0.794 -.3504544 .2680157
325 | .2401682 .106864 2.25 0.025 .0307186 .4496177
326 | .076578 .0550212 1.39 0.164 -.0312616 .1844176
327 | .1243334 .0598882 2.08 0.038 .0069546 .2417122
328 | .1518397 .1202955 1.26 0.207 -.0839351 .3876145
329 | .0879871 .074459 1.18 0.237 -.0579497 .233924
330 | .0965131 .0674135 1.43 0.152 -.035615 .2286412
331 | .1073542 .064149 1.67 0.094 -.0183754 .2330838
332 | .1056847 .0745504 1.42 0.156 -.0404314 .2518008
333 | .103763 .0889819 1.17 0.244 -.0706383 .2781642
334 | -.0137876 .0560447 -0.25 0.806 -.1236332 .0960581
335 | .0859808 .0610969 1.41 0.159 -.0337669 .2057285
336 | -.0037206 .0574649 -0.06 0.948 -.1163498 .1089086
337 | .043809 .066374 0.66 0.509 -.0862817 .1738997
338 | .0899456 .0620067 1.45 0.147 -.0315853 .2114764
339 | .0813804 .0556272 1.46 0.143 -.0276469 .1904077
340 | -.0408944 .0882156 -0.46 0.643 -.2137938 .1320051
341 | -.0238672 .0971233 -0.25 0.806 -.2142254 .166491
342 | -.0887995 .1908112 -0.47 0.642 -.4627825 .2851835
|
_cons | .3717256 .1484747 2.50 0.012 .0807205 .6627306
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic): 3172.597
Chi-sq(1) P-val = 0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic): 3.6e+04
(Kleibergen-Paap rk Wald F statistic): 1.1e+04
Stock-Yogo weak ID test critical values: 10% maximal IV size 16.38
15% maximal IV size 8.96
20% maximal IV size 6.66
25% maximal IV size 5.53
Source: Stock-Yogo (2005). Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments): 0.000
(equation exactly identified)
------------------------------------------------------------------------------
Instrumented: X1
Included instruments: C1 C2 C3 C4 C9 C10 C11 C12 C13 C14 C15 C1601 C17 C18
2010.year 2011.year 2012.year 2013.year 2014.year
2015.year 2016.year 2017.year 2018.year 2019.year
2.Industry 4.Industry 5.Industry 6.Industry 7.Industry
8.Industry 9.Industry 11.Industry 12.Industry 13.Industry
14.Industry 15.Industry 16.Industry 17.Industry
18.Industry 19.Industry 313.Industry 314.Industry
315.Industry 317.Industry 318.Industry 319.Industry
320.Industry 321.Industry 322.Industry 323.Industry
324.Industry 325.Industry 326.Industry 327.Industry
328.Industry 329.Industry 330.Industry 331.Industry
332.Industry 333.Industry 334.Industry 335.Industry
336.Industry 337.Industry 338.Industry 339.Industry
340.Industry 341.Industry 342.Industry
Excluded instruments: IV
------------------------------------------------------------------------------
. est store m2
. *结构，单变量回归，有固定效应
. ivreg2 Y2 ( X1 = IV ) i.year i.Industry ,r
IV (2SLS) estimation
--------------------
Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity
Number of obs = 17456
F( 56, 17399) = 12.21
Prob > F = 0.0000
Total (centered) SS = 477.2644587 Centered R2 = 0.0346
Total (uncentered) SS = 501.8215095 Uncentered R2 = 0.0819
Residual SS = 460.7456053 Root MSE = .1625
------------------------------------------------------------------------------
| Robust
Y2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X1 | .1670136 .0332341 5.03 0.000 .1018759 .2321513
|
year |
2010 | .001188 .0029923 0.40 0.691 -.0046768 .0070528
2011 | .0022476 .0030485 0.74 0.461 -.0037273 .0082226
2012 | .0132255 .003904 3.39 0.001 .0055738 .0208772
2013 | .021037 .0044296 4.75 0.000 .0123552 .0297189
2014 | .0293173 .0046499 6.30 0.000 .0202037 .0384309
2015 | .0489674 .0055681 8.79 0.000 .038054 .0598807
2016 | .0395976 .0050459 7.85 0.000 .0297079 .0494874
2017 | .0434521 .0049567 8.77 0.000 .0337372 .0531671
2018 | .0428529 .0045765 9.36 0.000 .0338831 .0518227
2019 | .0444201 .0045664 9.73 0.000 .0354702 .05337
|
Industry |
2 | -.010236 .0087481 -1.17 0.242 -.027382 .00691
4 | -.0132438 .0083741 -1.58 0.114 -.0296568 .0031692
5 | .0045767 .0100594 0.45 0.649 -.0151394 .0242929
6 | .0015924 .0086435 0.18 0.854 -.0153486 .0185333
7 | -.0111319 .0086495 -1.29 0.198 -.0280847 .0058209
8 | -.0171701 .0096465 -1.78 0.075 -.0360768 .0017366
9 | .0562214 .0117725 4.78 0.000 .0331478 .079295
11 | -.0048733 .0085414 -0.57 0.568 -.0216141 .0118674
12 | .0054013 .0140156 0.39 0.700 -.0220688 .0328714
13 | .0353429 .0228982 1.54 0.123 -.0095368 .0802225
14 | .0234943 .0160451 1.46 0.143 -.0079535 .0549422
15 | .0018643 .0080176 0.23 0.816 -.01385 .0175785
16 | -.0370811 .0083173 -4.46 0.000 -.0533827 -.0207795
17 | -.0001797 .0291625 -0.01 0.995 -.0573371 .0569778
18 | -.0197338 .0103137 -1.91 0.056 -.0399484 .0004807
19 | -.0057408 .0093605 -0.61 0.540 -.0240871 .0126055
313 | .0194871 .0127414 1.53 0.126 -.0054857 .0444598
314 | .0170134 .0148413 1.15 0.252 -.012075 .0461018
315 | -.008166 .0095393 -0.86 0.392 -.0268626 .0105307
317 | .0120177 .0119321 1.01 0.314 -.0113687 .035404
318 | .0015413 .0124345 0.12 0.901 -.0228299 .0259125
319 | .0493172 .028916 1.71 0.088 -.0073571 .1059914
320 | .1351639 .04401 3.07 0.002 .048906 .2214218
321 | .1567864 .0411135 3.81 0.000 .0762055 .2373673
322 | .032652 .0165003 1.98 0.048 .0003119 .064992
323 | .0289509 .0267591 1.08 0.279 -.023496 .0813977
324 | .0235132 .0278303 0.84 0.398 -.0310332 .0780596
325 | .0295713 .017366 1.70 0.089 -.0044654 .063608
326 | .0198447 .0093314 2.13 0.033 .0015554 .038134
327 | .0228359 .0096709 2.36 0.018 .0038813 .0417905
328 | .0327189 .0186689 1.75 0.080 -.0038714 .0693092
329 | .0180499 .0125935 1.43 0.152 -.006633 .0427327
330 | .0489335 .0126715 3.86 0.000 .0240978 .0737692
331 | -.0070441 .0090571 -0.78 0.437 -.0247957 .0107075
332 | .0150278 .0113579 1.32 0.186 -.0072333 .0372888
333 | .026679 .0136463 1.96 0.051 -.0000672 .0534253
334 | .009185 .0097455 0.94 0.346 -.0099158 .0282859
335 | .0155683 .0101355 1.54 0.125 -.0042969 .0354335
336 | -.0027172 .0092278 -0.29 0.768 -.0208033 .015369
337 | -.0077879 .0099652 -0.78 0.435 -.0273193 .0117436
338 | .0262637 .0099602 2.64 0.008 .0067421 .0457854
339 | .0329111 .0094846 3.47 0.001 .0143216 .0515006
340 | .0429468 .0209675 2.05 0.041 .0018512 .0840423
341 | .0034322 .0145753 0.24 0.814 -.0251347 .0319992
342 | .0267237 .0404131 0.66 0.508 -.0524845 .105932
|
_cons | -.013928 .0079483 -1.75 0.080 -.0295064 .0016504
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic): 3056.922
Chi-sq(1) P-val = 0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic): 4.1e+04
(Kleibergen-Paap rk Wald F statistic): 1.2e+04
Stock-Yogo weak ID test critical values: 10% maximal IV size 16.38
15% maximal IV size 8.96
20% maximal IV size 6.66
25% maximal IV size 5.53
Source: Stock-Yogo (2005). Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments): 0.000
(equation exactly identified)
------------------------------------------------------------------------------
Instrumented: X1
Included instruments: 2010.year 2011.year 2012.year 2013.year 2014.year
2015.year 2016.year 2017.year 2018.year 2019.year
2.Industry 4.Industry 5.Industry 6.Industry 7.Industry
8.Industry 9.Industry 11.Industry 12.Industry 13.Industry
14.Industry 15.Industry 16.Industry 17.Industry
18.Industry 19.Industry 313.Industry 314.Industry
315.Industry 317.Industry 318.Industry 319.Industry
320.Industry 321.Industry 322.Industry 323.Industry
324.Industry 325.Industry 326.Industry 327.Industry
328.Industry 329.Industry 330.Industry 331.Industry
332.Industry 333.Industry 334.Industry 335.Industry
336.Industry 337.Industry 338.Industry 339.Industry
340.Industry 341.Industry 342.Industry
Excluded instruments: IV
------------------------------------------------------------------------------
. est store m3
. *结构，有控制变量，有固定效应
. ivreg2 Y2 $CV2 ( X1 = IV ) i.year i.Industry ,r
IV (2SLS) estimation
--------------------
Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity
Number of obs = 17456
F( 70, 17385) = 10.36
Prob > F = 0.0000
Total (centered) SS = 477.2644587 Centered R2 = 0.0577
Total (uncentered) SS = 501.8215095 Uncentered R2 = 0.1038
Residual SS = 449.718548 Root MSE = .1605
------------------------------------------------------------------------------
| Robust
Y2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X1 | .1364008 .035294 3.86 0.000 .0672258 .2055758
C5 | .0036657 .001342 2.73 0.006 .0010354 .006296
C6 | .2021486 .0243317 8.31 0.000 .1544593 .2498378
C7 | .0286739 .0071352 4.02 0.000 .0146892 .0426587
C8 | .0040372 .0026554 1.52 0.128 -.0011672 .0092417
C9 | -.0062527 .0993852 -0.06 0.950 -.2010442 .1885388
C10 | .0006671 .0013652 0.49 0.625 -.0020087 .0033428
C11 | -.0013504 .000229 -5.90 0.000 -.0017992 -.0009015
C12 | -.0381238 .0027782 -13.72 0.000 -.043569 -.0326785
C13 | .0003337 .000726 0.46 0.646 -.0010893 .0017566
C14 | -.0055895 .0254805 -0.22 0.826 -.0555303 .0443513
C15 | -.0002006 .0036733 -0.05 0.956 -.0074002 .0069991
C1601 | -.0007935 .0002946 -2.69 0.007 -.0013709 -.0002161
C17 | .0001694 .0017063 0.10 0.921 -.0031749 .0035138
C18 | .0003206 .0010033 0.32 0.749 -.0016458 .002287
|
year |
2010 | -.0022385 .0032387 -0.69 0.489 -.0085863 .0041092
2011 | .000173 .0032635 0.05 0.958 -.0062234 .0065694
2012 | .011573 .0040627 2.85 0.004 .0036103 .0195356
2013 | .0191204 .0045693 4.18 0.000 .0101646 .0280761
2014 | .0279032 .004774 5.84 0.000 .0185462 .0372601
2015 | .0488344 .0060901 8.02 0.000 .0368979 .0607708
2016 | .0383213 .0054794 6.99 0.000 .0275819 .0490606
2017 | .0412816 .0054632 7.56 0.000 .0305739 .0519893
2018 | .0427632 .0053176 8.04 0.000 .0323409 .0531856
2019 | .0431146 .0059922 7.20 0.000 .0313702 .0548591
|
Industry |
2 | -.0060952 .0088273 -0.69 0.490 -.0233964 .0112061
4 | -.0009559 .0083137 -0.11 0.908 -.0172504 .0153386
5 | -.0057215 .0100531 -0.57 0.569 -.0254252 .0139821
6 | .0052491 .0084927 0.62 0.537 -.0113962 .0218944
7 | -.0010199 .0086271 -0.12 0.906 -.0179288 .0158889
8 | .0017083 .009537 0.18 0.858 -.0169838 .0204004
9 | .0478268 .0116334 4.11 0.000 .0250257 .0706279
11 | -.006693 .0084878 -0.79 0.430 -.0233288 .0099428
12 | .0024789 .0139112 0.18 0.859 -.0247865 .0297442
13 | .0262399 .0227689 1.15 0.249 -.0183863 .0708661
14 | .0254903 .0156916 1.62 0.104 -.0052646 .0562451
15 | .0008621 .0112908 0.08 0.939 -.0212674 .0229916
16 | -.0323139 .01319 -2.45 0.014 -.0581659 -.006462
17 | -.0226382 .0297202 -0.76 0.446 -.0808888 .0356124
18 | -.0119395 .0103461 -1.15 0.248 -.0322175 .0083385
19 | .0041952 .0092186 0.46 0.649 -.0138729 .0222634
313 | .0079418 .0124795 0.64 0.525 -.0165176 .0324013
314 | .012852 .0147049 0.87 0.382 -.015969 .0416731
315 | -.0033457 .0095747 -0.35 0.727 -.0221118 .0154203
317 | .0067821 .0117399 0.58 0.563 -.0162277 .0297918
318 | -.0182969 .0124644 -1.47 0.142 -.0427267 .0061328
319 | .0290315 .0283894 1.02 0.306 -.0266107 .0846738
320 | .119361 .0434385 2.75 0.006 .034223 .204499
321 | .1225842 .0409974 2.99 0.003 .0422308 .2029376
322 | .0307505 .0160735 1.91 0.056 -.000753 .0622541
323 | .0033912 .0267812 0.13 0.899 -.049099 .0558813
324 | -.0006945 .0273671 -0.03 0.980 -.054333 .0529441
325 | .0403943 .016995 2.38 0.017 .0070847 .0737038
326 | .0174831 .0091451 1.91 0.056 -.000441 .0354073
327 | .013504 .0094313 1.43 0.152 -.0049809 .031989
328 | .0253812 .0183226 1.39 0.166 -.0105305 .061293
329 | .0069911 .0123739 0.56 0.572 -.0172613 .0312435
330 | .0449928 .0122535 3.67 0.000 .0209763 .0690093
331 | .0012554 .0091784 0.14 0.891 -.0167339 .0192447
332 | .0112031 .0111859 1.00 0.317 -.0107209 .0331271
333 | .0100955 .0135229 0.75 0.455 -.0164088 .0365998
334 | .007583 .0095524 0.79 0.427 -.0111392 .0263053
335 | .0115407 .0098761 1.17 0.243 -.0078161 .0308975
336 | -.0103463 .0091289 -1.13 0.257 -.0282386 .0075459
337 | .0038984 .0099107 0.39 0.694 -.0155263 .023323
338 | .0114643 .0098431 1.16 0.244 -.0078279 .0307565
339 | .0269265 .0093256 2.89 0.004 .0086487 .0452042
340 | .0326984 .0203514 1.61 0.108 -.0071897 .0725865
341 | -.0164421 .0146474 -1.12 0.262 -.0451505 .0122663
342 | .0138231 .0414234 0.33 0.739 -.0673654 .0950116
|
_cons | -.0438298 .0336728 -1.30 0.193 -.1098274 .0221677
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic): 3173.150
Chi-sq(1) P-val = 0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic): 3.6e+04
(Kleibergen-Paap rk Wald F statistic): 1.1e+04
Stock-Yogo weak ID test critical values: 10% maximal IV size 16.38
15% maximal IV size 8.96
20% maximal IV size 6.66
25% maximal IV size 5.53
Source: Stock-Yogo (2005). Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments): 0.000
(equation exactly identified)
------------------------------------------------------------------------------
Instrumented: X1
Included instruments: C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C1601 C17 C18
2010.year 2011.year 2012.year 2013.year 2014.year
2015.year 2016.year 2017.year 2018.year 2019.year
2.Industry 4.Industry 5.Industry 6.Industry 7.Industry
8.Industry 9.Industry 11.Industry 12.Industry 13.Industry
14.Industry 15.Industry 16.Industry 17.Industry
18.Industry 19.Industry 313.Industry 314.Industry
315.Industry 317.Industry 318.Industry 319.Industry
320.Industry 321.Industry 322.Industry 323.Industry
324.Industry 325.Industry 326.Industry 327.Industry
328.Industry 329.Industry 330.Industry 331.Industry
332.Industry 333.Industry 334.Industry 335.Industry
336.Industry 337.Industry 338.Industry 339.Industry
340.Industry 341.Industry 342.Industry
Excluded instruments: IV
------------------------------------------------------------------------------
. est store m4
3、Bartik 工具变量命令ssaggregate
Bartik 工具变量的 Stata 命令 ssaggregate
下载安装方法为：
. ssc install ssaggregate, replace
checking ssaggregate consistency and verifying not already installed...
installing into c:\ado\plus\...
installation complete.
查看帮助文件：
help ssaggregate
