连老师你好:
看到这样一个资料,如下,probit回归中自变量的估计系数大于1(按照probit原理,估计系数是概率,应该小于1)
边际效应也大于1
如何解释这个估计系数? 谢谢!
. sysuse auto.dta
. probit foreign gear_ratio
Iteration 0: log likelihood = -45.03321
Iteration 1: log likelihood = -22.664339
Iteration 2: log likelihood = -21.653347
Iteration 3: log likelihood = -21.641904
Iteration 4: log likelihood = -21.641897
Iteration 5: log likelihood = -21.641897
Probit regression Number of obs = 74
LR chi2(1) = 46.78
Prob > chi2 = 0.0000
Log likelihood = -21.641897 Pseudo R2 = 0.5194
------------------------------------------------------------------------------
foreign | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gear_ratio | 3.45954 .7132767 4.85 0.000 2.061543 4.857537
_cons | -11.44249 2.30258 -4.97 0.000 -15.95546 -6.929517
------------------------------------------------------------------------------
. margins, dydx( gear_ratio) at( gear_ratio=3.3)
Conditional marginal effects Number of obs = 74
Model VCE : OIM
Expression : Pr(foreign), predict()
dy/dx w.r.t. : gear_ratio
at : gear_ratio = 3.3
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gear_ratio | 1.37969 .2867635 4.81 0.000 .8176435 1.941736
------------------------------------------------------------------------------