Stata-Probit模型交互术语解释



在我的论文中,我目前正在调查排放对地区健康的影响。因变量是双类别的,取值为0(如果健康状况良好(和1(如果健康不佳(,但排放量和资本总额除外,每个变量都是类别的:

这里有一个示例回归:

probit health i.year i.region##emissions age educ smoker gender urban capita_gdp, robust 
nofvlabel allbaselevels
Probit regression                               Number of obs     =     67,041
Wald chi2(64)     =    5850.28
Prob > chi2       =     0.0000
Log pseudolikelihood = -43026.965               Pseudo R2         =     0.0660
-------------------------------------------------------------------------------------
|               Robust
health      |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
year |
1  |          0  (base)
2  |  -.0236149   .0290446    -0.81   0.416    -.0805412    .0333115
3  |  -.0552885   .0343119    -1.61   0.107    -.1225386    .0119615
4  |  -.7498958   .0521191   -14.39   0.000    -.8520474   -.6477442
|
region |
1  |          0  (base)
2  |   .3424928   .1944582     1.76   0.078    -.0386383     .723624
3  |   .6631291    .343445     1.93   0.054    -.0100107    1.336269
4  |   1.005453   .1809361     5.56   0.000     .6508251    1.360081
5  |   .5202438   .2705144     1.92   0.054    -.0099547    1.050442
6  |    .853456   .2053275     4.16   0.000     .4510215     1.25589
7  |   -1.32784   1.329886    -1.00   0.318    -3.934369    1.278688
8  |   .2074103   .5587633     0.37   0.710    -.8877457    1.302566
9  |   .8778635   1.005655     0.87   0.383    -1.093184    2.848911
10  |    .614019   .2058646     2.98   0.003     .2105317    1.017506
11  |   1.103564   .2395228     4.61   0.000     .6341078     1.57302
12  |  -.9928198   1.189953    -0.83   0.404    -3.325084    1.339444
13  |   .2024027   .3014841     0.67   0.502    -.3884953    .7933008
14  |   .8510637   .1966648     4.33   0.000     .4656078     1.23652
15  |  -.4685238   1.062594    -0.44   0.659    -2.551171    1.614123
16  |   .1222191   .4271317     0.29   0.775    -.7149435    .9593818
17  |   1.777416   .9296525     1.91   0.056    -.0446694    3.599502
18  |   .7016812   .3960197     1.77   0.076    -.0745032    1.477866
19  |   .2164103   .2324297     0.93   0.352    -.2391436    .6719642
20  |  -.8683004   2.079837    -0.42   0.676    -4.944707    3.208106
21  |   .6094313   .1969787     3.09   0.002     .2233601    .9955025
22  |   .4586692   .2175369     2.11   0.035     .0323048    .8850336
23  |   .1376296    .316405     0.43   0.664    -.4825129    .7577721
24  |   .8800929   .2139805     4.11   0.000     .4606989    1.299487
25  |   .5008748    .181908     2.75   0.006     .1443417    .8574079
26  |   .7885192   .2055236     3.84   0.000     .3857004    1.191338
27  |   .8370192   .2066431     4.05   0.000     .4320061    1.242032
28  |   .0342872   .3383975     0.10   0.919    -.6289597     .697534
|
emissions |   .2331187   .0475761     4.90   0.000     .1398713    .3263662
|
region#c.emissions|
1  |          0  (base)
2  |  -.1763598   .0473856    -3.72   0.000    -.2692338   -.0834858
3  |   .0902526   .3483855     0.26   0.796    -.5925705    .7730757
4  |  -.2545669   .0436166    -5.84   0.000    -.3400539   -.1690798
5  |  -.1903919   .0525988    -3.62   0.000    -.2934837   -.0873002
6  |  -.2595892   .0565328    -4.59   0.000    -.3703914    -.148787
7  |   .3660934   .3615611     1.01   0.311    -.3425534     1.07474
8  |  -.1810636   .0873587    -2.07   0.038    -.3522836   -.0098436
9  |  -.2360667   .2817683    -0.84   0.402    -.7883225     .316189
10  |  -.2362498   .0452001    -5.23   0.000    -.3248403   -.1476593
11  |  -.2986525   .0606014    -4.93   0.000    -.4174291    -.179876
12  |   .4210453   .4355456     0.97   0.334    -.4326084    1.274699
13  |  -.1393217    .063414    -2.20   0.028    -.2636109   -.0150324
14  |  -.2428271   .0452505    -5.37   0.000    -.3315166   -.1541377
15  |  -.1078827   .1281398    -0.84   0.400     -.359032    .1432667
16  |  -.1121361   .0991541    -1.13   0.258    -.3064746    .0822024
17  |  -.3670531   .1360779    -2.70   0.007    -.6337609   -.1003453
18  |   -.241021   .1572069    -1.53   0.125    -.5491408    .0670988
19  |  -.2128744   .0452858    -4.70   0.000    -.3016328   -.1241159
20  |    .103139   .4313025     0.24   0.811    -.7421983    .9484763
21  |   -.217597   .0532092    -4.09   0.000    -.3218851   -.1133089
22  |  -.1796928   .0509009    -3.53   0.000    -.2794568   -.0799288
23  |  -.1510797   .0529603    -2.85   0.004    -.2548799   -.0472795
24  |  -.2589344   .0509662    -5.08   0.000    -.3588264   -.1590425
25  |   -.231851   .0448358    -5.17   0.000    -.3197276   -.1439745
26  |  -.2411263   .0442314    -5.45   0.000    -.3278182   -.1544344
27  |  -.2452313   .0465597    -5.27   0.000    -.3364867    -.153976
28  |  -.0563099   .1191566    -0.47   0.637    -.2898525    .1772328
|
age |   .1085835   .0049886    21.77   0.000      .098806    .1183609
educ |  -.1802489   .0107034   -16.84   0.000    -.2012272   -.1592707
smoker |    .080728   .0145963     5.53   0.000     .0521198    .1093362
gender |  -.2019473   .0145416   -13.89   0.000    -.2304483   -.1734463
urban |  -.1362217   .0112233   -12.14   0.000    -.1582189   -.1142245
capita_gdp |  -8.36e-06   .0000194    -0.43   0.667    -.0000464    .0000297
_cons |  -.4987429   .1638654    -3.04   0.002    -.8199132   -.1775726
-------------------------------------------------------------------------------------

我的问题是,我如何准确地解释因变量上的排放系数和区域.c#排放的相互作用?据我所知,区域1的排放系数是基准水平,区域2的排放系数比区域1低-1.176?

正确。另外两件事值得注意:

  1. 交互是双向的。因此,相互作用系数告诉你,第二区域的排放效应较小0.176,但如果排放量大一个单位,第二地区的排放效应也较小0.176。这也意味着你不能直接解释相互作用(区域和排放(中涉及的任何系数,因为它们都相互依赖
  2. Stata有一个出色的marginsmarginsplot命令,可以为您计算特定区域和/或排放水平下的系数。它有一点学习曲线,但如果你掌握了它的窍门,你可以制作出漂亮的图表来说明交互效果,这将比长回归表提供更多信息。网上有很多关于如何使用边距的教程,还有Ben Jann的演示

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