I am working on model that involves a dummy dependent variable with probabilty of occurance of event (0,1) and ordinal independent variables (with the value increasing with the number of times another event happened) along with dummy control variables. Moreover, I am analyzing panel data. which model should i use?!


You could choose among using the Linear Probability, the Logit/Logistic or the Probit models.

The choice between those two models depends on the data you're going to analyze and the assumptions you assume to get the model estimates.

IMHO, you should generally use the Logistic model, as you suggested in the question, since it has more relaxed assumptions and more fat-tails distribution with respect to the normal distribution assumed in the LPM (Linear Probability Model) and probit model. I'd avoid using the LPM because it does not often estimate the predicted probability bounded between a range from 0 and 1, as it should be.

Hope this helps.

  • $\begingroup$ I there any other way to do it? $\endgroup$ – Asad Jun 24 '15 at 16:21
  • $\begingroup$ Yes, @user80489, but they are a little bit more complicated. For instance, you could use a neural network too, but IMHO it is better to use simpler to do that. If you can tell me the program you're using I can suggest some example of how to do that. $\endgroup$ – Quantopik Jun 24 '15 at 16:37
  • $\begingroup$ i am using SAS to run these regressions $\endgroup$ – Asad Jun 24 '15 at 20:42
  • $\begingroup$ @user80489, if you own SAS base only, it is convenient to adopt the logistic model and you can find here support.sas.com/documentation/cdl/en/statug/63347/HTML/default/…. $\endgroup$ – Quantopik Jun 24 '15 at 20:48

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