Dear Profs and colleagues,
Could you please have a look at the 2sls results and say why is a potential reason for such a huge coefficient?
appreciated.
Cheers,
Paris
Could you please have a look at the 2sls results and say why is a potential reason for such a huge coefficient?
Code:
. ivregress 2sls ln_mig_firm (immi_sh =S_tfp_lp_iv) firm_age foreign_aff i.year i.sector i.region ,first
> vce(robust)
First-stage regressions
-----------------------
Number of obs = 26,491
F(25, 26465) = 181421.09
Prob > F = 0.0000
R-squared = 0.9932
Adj R-squared = 0.9932
Root MSE = 0.0118
------------------------------------------------------------------------------
| Robust
immi_sh | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
firm_age | 6.38e-06 4.55e-06 1.40 0.160 -2.53e-06 .0000153
foreign_aff | -.0006675 .0002654 -2.51 0.012 -.0011877 -.0001472
|
year |
2011 | -.0046915 .0000763 -61.49 0.000 -.0048411 -.004542
2012 | -.0188105 .0001383 -135.99 0.000 -.0190816 -.0185394
2013 | -.0291766 .0001304 -223.76 0.000 -.0294322 -.028921
2014 | -.0344718 .0001383 -249.22 0.000 -.034743 -.0342007
2015 | -.039164 .0001847 -212.08 0.000 -.0395259 -.038802
2016 | -.033093 .0001741 -190.12 0.000 -.0334342 -.0327519
2017 | -.0170869 .0001444 -118.37 0.000 -.0173698 -.016804
2018 | .0242047 .0002957 81.87 0.000 .0236252 .0247842
2019 | .0992518 .0008146 121.84 0.000 .0976552 .1008484
|
sector |
6 | .0000934 .0002583 0.36 0.718 -.0004129 .0005998
7 | .0006499 .0002023 3.21 0.001 .0002534 .0010465
9 | .0005937 .0002351 2.53 0.012 .0001329 .0010546
10 | .0016304 .0006251 2.61 0.009 .0004052 .0028556
11 | .0011972 .0006038 1.98 0.047 .0000138 .0023807
12 | .0006344 .0003622 1.75 0.080 -.0000754 .0013443
13 | .0001044 .0003756 0.28 0.781 -.0006317 .0008406
|
region |
2 | -.0182584 .0001484 -123.02 0.000 -.0185493 -.0179675
3 | .280337 .000202 1387.83 0.000 .2799411 .2807329
4 | .1460576 .0002521 579.29 0.000 .1455634 .1465517
5 | -.0667314 .000519 -128.59 0.000 -.0677486 -.0657142
6 | -.1121326 .0014834 -75.59 0.000 -.1150402 -.109225
7 | .0754772 .0004175 180.78 0.000 .0746588 .0762955
|
S_tfp_lp_iv | -1.49e-08 3.33e-08 -0.45 0.653 -8.02e-08 5.03e-08
_cons | .2246455 .000314 715.52 0.000 .2240301 .2252609
------------------------------------------------------------------------------
Instrumental variables 2SLS regression Number of obs = 26,491
Wald chi2(25) = 2.05
Prob > chi2 = 1.0000
R-squared = .
Root MSE = 41.91
------------------------------------------------------------------------------
| Robust
ln_mig_firm | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
immi_sh | 3559.456 7916.329 0.45 0.653 -11956.26 19075.18
firm_age | -.0215857 .0543908 -0.40 0.691 -.1281897 .0850183
foreign_aff | 1.837859 5.449556 0.34 0.736 -8.843074 12.51879
|
year |
2011 | 16.70971 37.10893 0.45 0.653 -56.02246 89.44188
2012 | 66.95652 148.8554 0.45 0.653 -224.7947 358.7077
2013 | 103.8499 230.9159 0.45 0.653 -348.7369 556.4367
2014 | 122.7141 272.8344 0.45 0.653 -412.0315 657.4598
2015 | 139.4373 309.9755 0.45 0.653 -468.1036 746.9781
2016 | 117.8588 261.917 0.45 0.653 -395.489 631.2066
2017 | 60.9297 135.2064 0.45 0.652 -204.0699 325.9293
2018 | -85.96534 191.6586 -0.45 0.654 -461.6094 289.6787
2019 | -352.9831 785.7056 -0.45 0.653 -1892.938 1186.972
|
sector |
6 | -.2117265 1.154057 -0.18 0.854 -2.473637 2.050184
7 | -2.544629 5.07718 -0.50 0.616 -12.49572 7.406461
9 | -1.912517 4.67503 -0.41 0.682 -11.07541 7.250373
10 | -6.121399 13.12657 -0.47 0.641 -31.849 19.6062
11 | -4.624659 9.580822 -0.48 0.629 -23.40272 14.15341
12 | -2.685645 5.163709 -0.52 0.603 -12.80633 7.435039
13 | .1726301 1.554906 0.11 0.912 -2.87493 3.22019
|
region |
2 | 65.09497 144.5012 0.45 0.652 -218.1222 348.3122
3 | -997.353 2219.142 -0.45 0.653 -5346.792 3352.086
4 | -519.5848 1156.363 -0.45 0.653 -2786.016 1746.846
5 | 237.6837 528.1781 0.45 0.653 -797.5264 1272.894
6 | 398.9299 887.5722 0.45 0.653 -1340.68 2138.539
7 | -268.7247 597.6235 -0.45 0.653 -1440.045 902.5958
|
_cons | -798.4771 1778.322 -0.45 0.653 -4283.924 2686.969
------------------------------------------------------------------------------
Instrumented: immi_sh
Instruments: firm_age foreign_aff 2011.year 2012.year 2013.year 2014.year
2015.year 2016.year 2017.year 2018.year 2019.year 6.sector
7.sector 9.sector 10.sector 11.sector 12.sector 13.sector
2.region 3.region 4.region 5.region 6.region 7.region
S_tfp_lp_iv
. dataex ln_mig_firm immi_sh S_tfp_lp_iv firm_age foreign_aff year sector region
* Example generated by -dataex-. For more info, type help dataex
clear
input float(ln_mig_firm immi_sh S_tfp_lp_iv) double firm_age float foreign_aff double year float(sector region)
0 .3650748 0 29 1 2010 3 4
.6931472 .3646793 0 30 1 2011 3 4
0 .343915 0 31 1 2012 3 4
0 .3277757 0 34 1 2015 3 4
0 .3433293 0 35 1 2016 3 4
0 .3693548 0 36 1 2017 3 4
0 .2298563 0 35 1 2010 9 1
0 .22289327 0 36 1 2011 9 1
0 .21112 0 37 1 2012 9 1
0 .19803737 0 38 1 2013 9 1
.6931472 .50198424 0 3 1 2010 9 3
1.0986123 .4977463 0 4 1 2011 9 3
.6931472 .4793266 0 5 1 2012 9 3
0 .4701448 0 6 1 2013 9 3
.6931472 .4650751 0 7 1 2014 9 3
.6931472 .4563765 0 8 1 2015 9 3
.6931472 .4846899 0 10 1 2017 9 3
0 .54520756 0 11 1 2018 9 3
2.0794415 .2298563 0 151 0 2010 3 1
2.0794415 .22289327 0 152 0 2011 3 1
1.3862944 .21112 0 153 0 2012 3 1
1.3862944 .19803737 0 154 0 2013 3 1
.6931472 .1918098 0 155 0 2014 3 1
.6931472 .1883774 0 156 0 2015 3 1
1.0986123 .19334607 0 157 0 2016 3 1
1.3862944 .2061446 0 158 0 2017 3 1
1.609438 .24056286 0 159 0 2018 3 1
1.0986123 .3039402 0 160 0 2019 3 1
0 .50198424 0 86 1 2010 3 3
0 .4563765 0 91 1 2015 3 3
.6931472 .4628399 0 92 1 2016 3 3
.6931472 .4846899 0 93 1 2017 3 3
1.0986123 .50198424 0 85 1 2010 7 3
0 .24056286 0 78 1 2018 7 1
0 .2298563 0 37 1 2010 3 1
0 .22289327 0 38 1 2011 3 1
0 .21112 0 39 1 2012 3 1
0 .19803737 0 40 1 2013 3 1
0 .1918098 0 41 1 2014 3 1
0 .1882468 0 74 1 2017 3 2
0 .21992885 0 75 1 2018 3 2
.6931472 .278263 0 76 1 2019 3 2
0 .1882468 0 52 1 2017 3 2
0 .21992885 0 53 1 2018 3 2
0 .278263 0 54 1 2019 3 2
1.0986123 .50198424 0 38 1 2010 7 3
1.3862944 .4977463 0 39 1 2011 7 3
0 .4701448 0 41 1 2013 7 3
1.0986123 .50198424 0 37 1 2010 6 3
0 .2298563 0 39 1 2010 3 1
1.3862944 .21017927 0 81 1 2010 3 2
.6931472 .19334395 0 83 1 2012 3 2
0 .18194366 0 84 1 2013 3 2
0 .1767448 0 85 1 2014 3 2
.6931472 .1733526 0 86 1 2015 3 2
.6931472 .2298563 0 40 1 2010 7 1
0 .2298563 0 92 1 2010 3 1
0 .22289327 0 93 1 2011 3 1
0 .21112 0 94 1 2012 3 1
0 .19334607 0 98 1 2016 3 1
0 .2061446 0 99 1 2017 3 1
0 .24056286 0 100 1 2018 3 1
.6931472 .3039402 0 101 1 2019 3 1
0 .2298563 7794.267 53 1 2010 3 1
0 .22289327 7557.628 54 1 2011 3 1
0 .21112 7157.588 55 1 2012 3 1
0 .19803737 6713.168 56 1 2013 3 1
0 .1918098 6501.658 57 1 2014 3 1
0 .1883774 6385.091 58 1 2015 3 1
0 .19334607 6553.832 59 1 2016 3 1
0 .21017927 0 56 1 2010 3 2
.6931472 .15304422 0 53 1 2010 3 5
.6931472 .15222874 0 54 1 2011 3 5
.6931472 .1431196 0 55 1 2012 3 5
.6931472 .13922645 0 56 1 2013 3 5
.6931472 .13559788 0 57 1 2014 3 5
0 .21017927 4804.0166 56 1 2010 3 2
.6931472 .4977463 0 56 1 2011 3 3
1.0986123 .4793266 0 57 1 2012 3 3
1.0986123 .4701448 0 58 1 2013 3 3
1.0986123 .4650751 0 59 1 2014 3 3
.6931472 .4563765 0 60 1 2015 3 3
1.0986123 .4846899 0 62 1 2017 3 3
.6931472 .54520756 0 63 1 2018 3 3
0 .6566781 0 64 1 2019 3 3
0 .22289327 0 53 1 2011 3 1
1.94591 .21017927 0 54 1 2010 3 2
1.3862944 .20349567 0 55 1 2011 3 2
1.0986123 .19334395 0 56 1 2012 3 2
2.3025851 .18194366 0 57 1 2013 3 2
1.7917595 .1767448 0 58 1 2014 3 2
2.0794415 .1733526 0 59 1 2015 3 2
1.94591 .17714684 0 60 1 2016 3 2
.6931472 .50198424 0 52 1 2010 13 3
.6931472 .2298563 0 53 0 2010 7 1
1.3862944 .1918098 0 57 0 2014 7 1
1.7917595 .1883774 0 58 0 2015 7 1
2.0794415 .2298563 0 69 1 2010 3 1
1.7917595 .22289327 0 70 1 2011 3 1
0 .21112 0 71 1 2012 3 1
end
Cheers,
Paris
Comment