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雷达卡
ivprobit结果是这样的
Checking reduced-form model...
first-stage regression
Source | SS df MS Number of obs = 1,544
-------------+---------------------------------- F(14, 1529) = 44.01
Model | 1087.33046 14 77.6664611 Prob > F = 0.0000
Residual | 2698.2233 1,529 1.76469804 R-squared = 0.2872
-------------+---------------------------------- Adj R-squared = 0.2807
Total | 3785.55376 1,543 2.45337249 Root MSE = 1.3284
------------------------------------------------------------------------------
e_scene | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
m_pnet | 1.697263 .2803529 6.05 0.000 1.147346 2.247179
h_age | -.024949 .0035732 -6.98 0.000 -.0319579 -.0179401
happiness | -.0371827 .0507445 -0.73 0.464 -.1367188 .0623535
lnh_edu | .1839193 .048722 3.77 0.000 .0883502 .2794883
h_health | -.072837 .0339737 -2.14 0.032 -.1394771 -.0061969
ln_income | .3171016 .0297509 10.66 0.000 .2587447 .3754585
older | -.9904885 .1660449 -5.97 0.000 -1.316188 -.6647886
peo_all | .0482668 .0206213 2.34 0.019 .0078177 .0887159
|
县代码 |
5202 | -.1234581 .1281845 -0.96 0.336 -.3748942 .127978
5301 | -.226968 .1285964 -1.76 0.078 -.479212 .0252759
5302 | -.1837638 .1297395 -1.42 0.157 -.43825 .0707224
6101 | -.0160559 .1271677 -0.13 0.900 -.2654974 .2333857
6102 | -.0242761 .1288075 -0.19 0.851 -.2769342 .228382
6201 | .1520308 .1328649 1.14 0.253 -.108586 .4126476
|
_cons | -.504876 .4482892 -1.13 0.260 -1.384203 .3744508
------------------------------------------------------------------------------
Two-step probit with endogenous regressors Number of obs = 1,544
Wald chi2(14) = 216.18
Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
e_scene | .5354411 .2270394 2.36 0.018 .0904521 .9804302
h_age | -.0088357 .0073706 -1.20 0.231 -.0232819 .0056105
happiness | -.0155068 .0696044 -0.22 0.824 -.1519289 .1209154
lnh_edu | .0636106 .0799028 0.80 0.426 -.0929961 .2202172
h_health | -.0821827 .0488204 -1.68 0.092 -.1778689 .0135036
ln_income | .22098 .0856846 2.58 0.010 .0530412 .3889188
older | -.4678875 .3823308 -1.22 0.221 -1.217242 .281467
peo_all | .041224 .0294942 1.40 0.162 -.0165835 .0990315
|
县代码 |
5202 | -.1761632 .1743886 -1.01 0.312 -.5179585 .1656321
5301 | -.2335821 .1750174 -1.33 0.182 -.57661 .1094458
5302 | -.2354332 .1904669 -1.24 0.216 -.6087414 .1378751
6101 | .0389248 .1668802 0.23 0.816 -.2881545 .366004
6102 | .0563485 .1720591 0.33 0.743 -.2808811 .3935781
6201 | -.0603093 .1715669 -0.35 0.725 -.3965742 .2759556
|
_cons | -3.171017 .5856556 -5.41 0.000 -4.318881 -2.023153
------------------------------------------------------------------------------
Wald test of exogeneity: chi2(1) = 2.92 Prob > chi2 = 0.0873
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
.................................................. 100
Weak instrument robust tests and confidence sets for IV probit
H0: beta[dig_finance:e_scene] = 0
------------------------------------------------------------------------------
Test | Statistic p-value | Conf. level Conf. Set
------+---------------------------------+-------------------------------------
AR | chi2(1) = 7.12 0.0076 | 95% [ .201267, .958652]
------+---------------------------------+-------------------------------------
Wald | chi2(1) = 9.93 0.0016 | 95% [ .227776, .977587]
------------------------------------------------------------------------------
Confidence sets estimated for 100 points in [ -.14713, 1.35249].
Method = minimum distance (MD).
Tests assume i.i.d. errors. Small sample adjustments were used.
Wald statistic in last row is based on ivprobit estimation and is not robust to weak instruments.
请问在论文表格中要把哪些数据列出来呢？另外最后两行是什么错误呢？求助各位大佬
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