两次回归结果不一样,第一次是OFDI和GDP,第二次是OFDI和GDP和Inslogit OFDI GDP_Per Ins_Dis
Iteration 0: log likelihood = -968.97558
Iteration 1: log likelihood = -944.80815
Iteration 2: log likelihood = -944.27646
Iteration 3: log likelihood = -944.27634
Iteration 4: log likelihood = -944.27634
Logistic regression Number of obs = 1,750
LR chi2(2) = 49.40
Prob > chi2 = 0.0000
Log likelihood = -944.27634 Pseudo R2 = 0.0255
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OFDI | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
GDP_Per | .0000179 3.36e-06 5.34 0.000 .0000114 .0000245
Ins_Dis | .0220893 .0449115 0.49 0.623 -.0659355 .1101142
_cons | -1.332994 .083587 -15.95 0.000 -1.496822 -1.169167
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logit OFDI Ins_Dis
Iteration 0: log likelihood = -968.97558
Iteration 1: log likelihood = -958.8958
Iteration 2: log likelihood = -958.77287
Iteration 3: log likelihood = -958.77287
Logistic regression Number of obs = 1,750
LR chi2(1) = 20.41
Prob > chi2 = 0.0000
Log likelihood = -958.77287 Pseudo R2 = 0.0105
------------------------------------------------------------------------------
OFDI | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Ins_Dis | .1656136 .0360008 4.60 0.000 .0950533 .2361738
_cons | -1.40377 .0822577 -17.07 0.000 -1.564992 -1.242548
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