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kjetil b halvorsen
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I am performing a Logit Model on binary variable SALE, when running on stata some of my explanatory variables were dropped as they were "perfect predictors". after some reading i believe the problem to be that of separation. some authors propose Firths method to deal with this. I can install the stata extension and run the tests again no problem but i want to know:

  1. will this work for a relatively small dataset (n=207)?
  2. are there any downsides to this approach? (bias or overstatement of errors, anything like that)

Thanks!

I am performing a Logit Model on binary variable SALE, when running on stata some of my explanatory variables were dropped as they were "perfect predictors". after some reading i believe the problem to be that of separation. some authors propose Firths method to deal with this. I can install the stata extension and run the tests again no problem but i want to know:

  1. will this work for a relatively small dataset (n=207)?
  2. are there any downsides to this approach? (bias or overstatement of errors, anything like that)

Thanks!

I am performing a Logit Model on binary variable SALE, when running on stata some of my explanatory variables were dropped as they were "perfect predictors". after some reading i believe the problem to be that of separation. some authors propose Firths method to deal with this. I can install the stata extension and run the tests again no problem but i want to know:

  1. will this work for a relatively small dataset (n=207)?
  2. are there any downsides to this approach? (bias or overstatement of errors, anything like that)
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Tom Witten
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any problems with Firths Logit model (to deal with separation)

I am performing a Logit Model on binary variable SALE, when running on stata some of my explanatory variables were dropped as they were "perfect predictors". after some reading i believe the problem to be that of separation. some authors propose Firths method to deal with this. I can install the stata extension and run the tests again no problem but i want to know:

  1. will this work for a relatively small dataset (n=207)?
  2. are there any downsides to this approach? (bias or overstatement of errors, anything like that)

Thanks!