I have a dataset that, among other variables, has acceptance rate (as a percentage, 0-1 scale) and number of payments (0, 1, 2, 3). I want to assess whether or not the leniency in acceptances (that is, a higher acceptance rate) is associated with the likelihood that payments are made.
At first, I ran an ordered logit regression and calculated the marginal effects at the 25th, 50th, and 75 percentile of acceptance rate. However, because my dependent variable is a count, I'm thinking I may need to use a Poisson or negative binomial model. The mean of my DV is not equal to the variance, but I think to solve this, I can just use "poisson y x, vce(robust)."
This is the exact question: Present one table and one graph assessing whether the leniency of a firm is associated with the likelihood that renewal fees are paid.
When I run the poisson regression, I get this:
. poisson number_payments acceptance_rate, irr vce(robust)
Iteration 0: log pseudolikelihood = -6399.7702
Iteration 1: log pseudolikelihood = -6399.7702
Poisson regression Number of obs = 4,017
Wald chi2(1) = 13.32
Prob > chi2 = 0.0003
Log pseudolikelihood = -6399.7702 Pseudo R2 = 0.0006
---------------------------------------------------------------------------------
| Robust
number_payments | IRR Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
acceptance_rate | .8221783 .0441114 -3.65 0.000 .740112 .9133444
_cons | 2.44222 .0869474 25.08 0.000 2.277616 2.61872
---------------------------------------------------------------------------------
I'm not sure how to interpret this in the context of the question.
Thanks for your help!
At first, I ran an ordered logit regression and calculated the marginal effects at the 25th, 50th, and 75 percentile of acceptance rate. However, because my dependent variable is a count, I'm thinking I may need to use a Poisson or negative binomial model. The mean of my DV is not equal to the variance, but I think to solve this, I can just use "poisson y x, vce(robust)."
This is the exact question: Present one table and one graph assessing whether the leniency of a firm is associated with the likelihood that renewal fees are paid.
When I run the poisson regression, I get this:
. poisson number_payments acceptance_rate, irr vce(robust)
Iteration 0: log pseudolikelihood = -6399.7702
Iteration 1: log pseudolikelihood = -6399.7702
Poisson regression Number of obs = 4,017
Wald chi2(1) = 13.32
Prob > chi2 = 0.0003
Log pseudolikelihood = -6399.7702 Pseudo R2 = 0.0006
---------------------------------------------------------------------------------
| Robust
number_payments | IRR Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
acceptance_rate | .8221783 .0441114 -3.65 0.000 .740112 .9133444
_cons | 2.44222 .0869474 25.08 0.000 2.277616 2.61872
---------------------------------------------------------------------------------
I'm not sure how to interpret this in the context of the question.
Thanks for your help!
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